101206_MOUSSA_02TQBG01L06_TH

UNIVERSITÉ DE LA MÉDITERRANÉE (AIX-MARSEILLE II)Faculté des Sciences Economiques et de Gestion

Ecole Doctorale de Sciences Economiques et de Gestion d’Aix-Marseille n 372

Année 2010 Numéro attribué par la bibliothèque

| | | | | | | | | | | |

Thèse pour le Doctorat ès Sciences EconomiquesPrésentée et soutenue publiquement par

Zakaria MOUSSA

le 6 décembre 2010

——————————Assouplissement quantitatif ; quels enseignements

tirer de l’expérience japonaise ?——————————

Directeur de Thèse

M. Eric GIRARDIN, Professeur à l’Université de la Méditerranée, GREQAM

Jury

RapporteursM. Patrick FÈVE Professeur à l’université de Toulouse I, GREMAQM. Andrew J. FILARDO Economiste en Chef, Banque des Règlements

Internationaux, zone Asie–PacifiqueExaminateursM. Gilles DUFRÉNOT Professeur à l’Université d’Aix-Marseille 2, DEFIM. Michel LUBRANO Directeur de recherche CNRS, GREQAM,M. Benoît MOJON Banque de France, Chef du service de recherche

sur la politique monétaire

UNIVERSITÉ DE LA MÉDITERRANÉE (AIX-MARSEILLE II)Faculté des Sciences Economiques et de Gestion

Ecole Doctorale de Sciences Economiques et de Gestion d’Aix-Marseille n 372

Année 2010 Numéro attribué par la bibliothèque

| | | | | | | | | | | |

Thèse pour le Doctorat ès Sciences EconomiquesPrésentée et soutenue publiquement par

Zakaria MOUSSA

le 6 décembre 2010

——————————Assouplissement quantitatif ; quels enseignements

tirer de l’expérience japonaise ?——————————

Directeur de Thèse

M. Eric GIRARDIN, Professeur à l’Université de la Méditerranée, GREQAM

Jury

RapporteursM. Patrick FÈVE Professeur à l’université de Toulouse I, GREMAQM. Andrew J. FILARDO Economiste en Chef, Banque des Règlements

Internationaux, zone Asie–PacifiqueExaminateursM. Gilles DUFRÉNOT Professeur à l’Université d’Aix-Marseille 2, DEFIM. Michel LUBRANO Directeur de recherche CNRS, GREQAM,M. Benoît MOJON Banque de France, Chef du service de recherche

sur la politique monétaire

L’Université de la Méditerranée n’entend ni approuver, ni désapprouver les opinions partic-ulières du candidat: ces opinions doivent être considérées comme propres à leur auteur.

En souvenir de mon père.

A ma famille et à Gaëlle.

Résumé

La crise financière actuelle, en raison de sa similarité avec celle du Japon des années 1990,

a poussé les autorités monétaires des plus grandes banques centrales à adopter l’assouplis-

sement quantitatif. Seul le Japon, ayant connu une expérience d’assouplissement quantitatif

récente mais depuis suffisamment d’années pour être étudiée, peut fournir des éléments de

solution à cette crise.

Cette thèse applique les techniques économétriques les plus appropriées et récentes

à l’analyse de l’assouplissement quantitatif, appliqué par la Banque du Japon entre 2001 et

2006. En trois chapitres sont traitées les questions de savoir s’il était efficace ; sous quelles

conditions ? Par quels canaux ?

L’efficacité de cette stratégie de politique monétaire à stimuler l’activité et à stopper

la spirale déflationniste a été montrée. Cette expérience met en avant le rôle important que

la politique monétaire peut jouer pour sortir de la crise, même quand le taux directeur atteint

zéro. Le canal des anticipations comme le canal de rééquilibrage des portefeuilles ont tous

deux joué un rôle important dans la transmission de ces effets. Les principaux enseignements

que l’on peut tirer de l’expérience japonaise sont, d’abord de remédier radicalement et

immédiatement aux fragilités du secteur financier, deuxièmement, de mener une politique

monétaire particulièrement agressive. Enfin, d’attendre le temps nécessaire pour que les

fruits de cette politique viennent. L’expérience japonaise suggère que la Fed et la banque

d’Angleterre doivent reporter leur sortie de cette stratégie, sortie qui doit être menée dans

le cadre d’un programme et selon des objectifs numériques clairs.

Mots clés : Assouplissement quantitatif ; Canaux de transmission ; FAVAR ; Markov-

switching ; Time-varying-parameter FAVAR ; Modèle macro-finance ; Japon.

Abstract

The current financial crisis has now led most major central banks to rely on quantitative

easing. The unique Japanese experience of quantitative easing is the only experience which

enables us to judge this therapy’s effectiveness and the timing of the exit strategy. Is quan-

titative easing effective ? Under which conditions ? Through which canal ?

This thesis, consisting of three essays, applies appropriate and recent econometric

techniques to examine the quantitative easing in Japan between 2001 and 2006. We show,

for the first time, that quantitative easing was able not only to prevent further recession

and deflation but also to provide considerable stimulation to both output and prices. Moreo-

ver, both expectation and portfolio-rebalancing channels play a crucial role in transmitting

monetary policy effects. This experience shows that the monetary policy is still potent even

when short-term interest rates reach a zero lower bound.

The Japanese experience suggests that efforts to clean up the bank’s balance sheets

significantly improved the effectiveness of quantitative easing. However, this effect, although

considerable, was short-lived ; it became insignificant after one year. The short duration

of this effect confirms the wisdom of the Fed’s decision to maintain quantitative easing

longer, so that being short-lived, the positive effects could be exploited. In the light of the

Japanese experience, we argue that, in addition to their fast reaction and the huge amount of

CABs employed, which may have helped relieve short-term liquidity pressures in the financial

system, the Fed was better off postponing its exit from quantitative easing.

Keywords : Quantitative Easing Policy ; Transmission channels ; FAVAR ; Markov-

switching ; Time-varying-parameter FAVAR ; Macro-finance model ; Japan.

Remerciements

Tant de personnes ont rendu possible l’aboutissement de ce travail de thèse qu’il m’est

aujourd’hui difficile de n’en oublier aucune. Ce manuscrit conclut quatre ans de travail ; je

tiens en ces trop courtes lignes à exprimer ma reconnaissance envers tous ceux qui de près

ou de loin y ont contribué, et demande par avance excuse à ceux que j’aurais oubliés.

J’exprime en premier lieu ma gratitude à Eric Girardin, mon directeur de thèse, pour

m’avoir proposé ce sujet passionnant et m’avoir maintenu sa confiance tout au long de ces

années. Je n’oublie pas son premier message, décisif, où il me manifestait son intérêt et

présentait sa motivation pour un travail commun sur cette thèse. Merci aussi pour les pré-

cieux conseils qui ont suivi, sa constante disponibilité et sa gentillesse. J’ai particulièrement

apprécié les discussions scientifiques que nous avons eues et qui m’ont profondément aidé

à avancer sur le sujet. Merci également de m’avoir guidé, tout en me laissant l’autonomie

de choisir mon chemin et mes méthodes.

Pour avoir accepté de rapporter ce travail, j’assure toute ma reconnaissance à Patrick

Fève et à Andrew Filardo ; leurs rapports ont grandement contribué à améliorer mes travaux,

notamment du point de vue de l’interprétation des résultats des modèles exposés. Que soient

remerciés également les autres jurés pour avoir lu mon manuscrit et y avoir porté un regard

critique ; messieurs Michel Lubrano, Benoît Mojon et plus particulièrement Gilles Dufrénot

pour avoir assuré le rôle de président de jury.

Nombreux sont ceux à avoir, au fil de ma thèse, apporté leur contribution scientifique.

Je tiens ainsi à remercier Steve Basen, Anne Péguin, Costin Protopopescu et à nouveau

Michel Lubrano, pour leur aide en économétrie et leurs conseils avisés.

Ce travail a pu voir le jour grâce à un financement personnel, puis à l’obtention d’un

demi-poste d’ATER à l’Université Marseille 2 ; je tiens donc à exprimer ma gratitude aux

personnes qui m’ont aidé à atteindre ces conditions de travail idéales, notamment Domi-

nique Ami et Pierre Granier. Je garde de bons souvenirs de cette expérience durant laquelle

j’ai collaboré principalement avec Dominique, que je remercie énormément pour sa bonne

humeur, et le plaisir trouvé à travailler avec elle. Je tiens à remercier également les membres

de la Faculté des Sciences de Luminy pour leur accueil, leur soutien, et surtout pour m’avoir

renouvelé leur confiance une deuxième année ; cela m’a aidé à finir ma thèse dans de bonnes

conditions.

Ce travail de thèse a été un long parcours, au sens propre comme au figuré ; il m’a

même mené à l’autre bout du monde, au pays du soleil levant. Tout au long de mon séjour au

Japon, j’ai eu la chance de croiser des personnes de grande qualité, scientifique et humaine,

qui m’ont encouragé à continuer mon chemin de recherche et m’ont rendu confiance en moi

après une première période difficile. J’adresse mes vifs remerciements aux professeurs de

l’université de Musashi à Tokyo pour leur accueil, générosité et bonne humeur. Dans l’ordre

alphabétique (français), je remercie Kimihiro Furuse, Yoshio Kurosaka, Yuko Nikaido, Junko

Nishimura, Sanae Ohno et Eiko Sakai. Je tiens aussi à remercier Yuki Teranishi pour l’intérêt

qu’il a montré à l’égard de mon travail et son invitation au sein de la Banque du Japon,

ainsi que le reste de l’équipe pour ses remarques et suggestions durant ma présentation.

Je veux spécialement témoigner de ma reconnaissance à Yusho Kaglaoka qui, au delà de

son implication au chapitre 3 réalisé conjointement, n’a cessé de montrer sa disponibilité,

sa gentillesse, son souci de mon bien être et de ma bonne intégration au sein de l’équipe ;

que Yusho soit assuré de ma reconnaissance pour son indéfectible soutien, dans l’espoir que

nous retravaillons ensemble bientôt.

Bien sûr, ce séjour inoubliable au Japon a été facilité par l’aide financière procurée

par l’école doctorale (n 372) et par une bourse accordée dans le cadre du Groupement de

Recherche International en " Connaissance, interactions, décisions ". Je remercie donc Jean

Benoît Zimmerman, directeur du GREQAM et surtout Alain Vendetti pour m’avoir procuré

les informations utiles en temps et en heure et pour sa bonne humeur sportive et com-

municative. Je remercie également Nobuyuki Hanaki pour nos échanges franco-japonais de

rudiments linguistiques qui m’ont été d’une grande aide quotidienne pour entrer en contact

avec ses concitoyens.

J’adresse également ma profonde reconnaissance à tous les membres de l’équipe

administrative et informatique du GREQAM qui m’ont apporté leur indispensable soutien

logistique : Bernadette, Corinne, Gérald, Carole, Isabelle, Jean-Paul, Lydie, Pascal. Merci

d’avoir toujours reçu mes demandes avec le sourire et d’y avoir répondu avec autant d’effi-

cacité.

Une mention spéciale est donnée à Marjorie Sweetko pour l’aide irremplaçable qu’elle

a apportée à ma rédaction en anglais ; la tâche n’était pas aisée et elle l’a accomplie avec

une compétence et un dévouement remarquables, que je n’oublierai pas.

La bonne ambiance qui règne au GREQAM a accompagné la progression de ce tra-

vail ; mes collègues, et leur bonne humeur quotidienne ont grandement contribué à faire des

journées au laboratoire un plaisir. Je remercie Benoît S. pour son amitié et nos discussions,

Philippe pour sa gentillesse et pour les bons moments passés ensemble lors de notre collabo-

ration dans et en dehors du travail, Renaud pour son sens de l’humour et ses conseils avisés,

Sarra pour nos riches échanges et pour sa méticuleuse relecture de l’introduction, Luis pour

11

son sens de l’humour et son aide précieuse pour la présentation, Maame et Shamaila pour

leur relecture. Je tiens également à remercier Adreana, Agnès, Andreea, Aziz, Benoît T.,

Carmela, Chen, Clément, Elvera, Elsa, Gabriele, Gwenola, Jamel, Kalila, Kamila, , Katia,

Leila, Maame, Mandy, Maria, , Mathieu, Maty, Meriem, Morgane Nariné, , Ophélie, Paul,

Paul-Antoine, Rabeh, Sonia, Walid et Waqar pour leur soutien.

Mes remerciements vont aussi à mes voisins sociologues et anthropologues du centre

Norbert Elias avec qui j’ai vécu de très agréables et enrichissants moments pendant les repas

ou en dehors du cadre de travail. Je tiens donc à remercier particulièrement Jean-Christophe,

Tanguy et Jean-Baptiste pour leur bonne humeur et leurs discussions qui m’éloignaient

momentanément de l’économie. Je remercie également Cyril, Karim, Julie et Vincent pour

leurs encouragements en fin de parcours.

J’ai la chance d’avoir été solidement accompagné à chaque étape de ce périple ; ma

famille, bien qu’éloignée, a toujours été présente et c’est son appui qui m’a aidé à avancer.

Je voudrais remercier spécialement mon épouse, Gaëlle, qui a joué un rôle déterminant au

cours de ces années de thèse, et ce depuis le soir où le hasard nous a mené dans un restaurant

japonais pour y décider ensemble de débuter l’aventure de cette thèse. Elle a accompagné

mes enthousiasmes et mes angoisses, a supporté mes absences récurrentes et surtout m’a

aidé à surmonter les moments difficiles grâce à son soutien quotidien indéfectible, fourni

avec tout son amour.

Je dédie cette thèse à ma famille, avec tout mon cœur, en souvenir de mon père qui

a tant souhaité voir ses enfants aboutir dans leurs études, et qui a été pour moi un modèle

de travail, d’honnêteté et de persévérance. Mes remerciements vont en particulier à ma mère

pour son soutien discret et essentiel, à mes grandes sœurs, belles étoiles qui veillent sur moi,

ainsi qu’à tous mes frères pour avoir montré leur optimisme face au partage des difficultés.

J’adresse également ma profonde reconnaissance aux membres de ma belle-famille pour leurs

encouragements constants, leur accueil chaleureux et leur générosité.

Table des matières

Table des matières

Table des figures iii

Liste des tableaux v

Nomenclature vi

Introduction générale 1

Chapter 1: Quantitative easing works: Lessons from the unique experience in

Japan 23

1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

1.2 Related literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

1.3 Transmission Channels of QEMP . . . . . . . . . . . . . . . . . . . . . . . 29

1.4 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

1.4.1 MS-FAVAR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

1.4.2 Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

1.5 Empirical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

1.5.1 Estimated Structural Factors . . . . . . . . . . . . . . . . . . . . . 42

1.5.2 Traditional MS-VAR . . . . . . . . . . . . . . . . . . . . . . . . . 44

1.5.3 MS-FAVAR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

1.5.4 Is a fiscal stimulus effective? . . . . . . . . . . . . . . . . . . . . . 52

1.6 Robustness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

1.7 Implications and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . 56

1.8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

Appendices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

Chapter 2: Quantitative Easing under Scrutiny: A TVP-FAVAR Model 87

2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

2.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

2.2.1 TVP-FAVAR model . . . . . . . . . . . . . . . . . . . . . . . . . . 93

i

Table des matières

2.2.2 Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

2.3 Empirical results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102

2.3.1 Data and preliminary results . . . . . . . . . . . . . . . . . . . . . 102

2.3.2 Specification tests . . . . . . . . . . . . . . . . . . . . . . . . . . 103

2.3.3 The evolution of the Japanese monetary policy . . . . . . . . . . . 104

2.3.4 Impulse response analysis . . . . . . . . . . . . . . . . . . . . . . . 106

2.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114

Chapitre 3 : Quantitative Easing and the Time-Varying Dynamics of the Term

Structure of Interest rate in Japan 123

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123

3.2 Estimating spot rate curves for Japan . . . . . . . . . . . . . . . . . . . . 127

3.2.1 Data construction . . . . . . . . . . . . . . . . . . . . . . . . . . 127

3.2.2 Estimation procedure . . . . . . . . . . . . . . . . . . . . . . . . . 128

3.2.3 Summary statistics . . . . . . . . . . . . . . . . . . . . . . . . . . 131

3.3 Yield-Curve Fitting : The Macro-Finance Model . . . . . . . . . . . . . . . 133

3.3.1 Methodology and Estimation . . . . . . . . . . . . . . . . . . . . 133

3.3.2 Priors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137

3.4 Empirical results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138

3.4.1 Preliminary Empirical Results . . . . . . . . . . . . . . . . . . . . . 138

3.4.2 Evidence on the expectations hypothesis (EH) . . . . . . . . . . . 140

3.4.3 Time-varying term premium . . . . . . . . . . . . . . . . . . . . . 142

3.4.4 Empirical Results From the Macro-Finance Model . . . . . . . . . 144

3.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151

Conclusion générale 159

Bibliographie 165

ii

Table des figures

Table des figures

1 L’économie japonaise avant et après le dégonflement de la bulle spéculative 32 Stimulus fiscal et dette publique au Japon 1990-2008 . . . . . . . . . . . 43 Cibles sur le niveau des comptes courants des banques privées . . . . . . . 54 Créances douteuses des banques japonaises et pertes dues à ces créances . 65 Réaction de la politique monétaire après l’éclatement de la bulle financière

au Japon et dans le reste des pays du G7 . . . . . . . . . . . . . . . . . . 8

1.1 Regime probabilities for MSIAH-VAR . . . . . . . . . . . . . . . . . . . . 461.2 Response to a monetary base shock in MS-VAR regime 1 re-

gime 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 481.3 Regime probabilities for MS-FAVAR . . . . . . . . . . . . . . . . . . . . . 501.4 Response to a monetary base shock in MS-FAVAR . . . . . . . . . . . . . 511.5 Estimated factor loadings . . . . . . . . . . . . . . . . . . . . . . . . . . . 611.6 The original and corrected M0 . . . . . . . . . . . . . . . . . . . . . . . . 621.7 Activity factor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 691.8 Price factor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 701.9 Interest rate factor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 711.10 The JGB issuance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 791.11 Regimes probabilities - MS-FAVAR model . . . . . . . . . . . . . . . . . . 801.12 Response to a monetary base shock in MS-FAVAR . . . . . . . . . . . . . 811.13 Response to a fiscal policy shock in MS-FAVAR . . . . . . . . . . . . . . . 821.14 Response to a fiscal policy shock in MS-FAVAR . . . . . . . . . . . . . . . 83

2.1 Posterior mean of the standard deviation of equation residuals . . . . . . . 1052.2 Impulse response functions . . . . . . . . . . . . . . . . . . . . . . . . . . 1072.3 Impulse responses - Policy-duration effect . . . . . . . . . . . . . . . . . . 1102.4 Impulse responses - Portfolio-rebalancing channel . . . . . . . . . . . . . . 1122.5 Impulse responses - Disaggregated price . . . . . . . . . . . . . . . . . . . 1202.6 Impulse responses - Disaggregated production . . . . . . . . . . . . . . . . 121

3.1 Japanese Government Bond spot curves 1985-2009 . . . . . . . . . . . . . 1313.2 Estimated factors and their empirical counterparts . . . . . . . . . . . . . 1393.3 Estimated Standard deviation of the FAVAR residuals . . . . . . . . . . . . 1403.4 Extracted expectation component . . . . . . . . . . . . . . . . . . . . . . 1413.5 Estimated term premium . . . . . . . . . . . . . . . . . . . . . . . . . . . 1433.6 Unconditional variance - Call rate shock. . . . . . . . . . . . . . . . . . . . 1453.7 Impulse responses - Call rate shock . . . . . . . . . . . . . . . . . . . . . . 147

iii

Table des figures

3.8 Unconditional variance - level factor shock . . . . . . . . . . . . . . . . . . 1493.9 Impulse responses - Level shock . . . . . . . . . . . . . . . . . . . . . . . 1513.10 Variance decomposition due to inflation . . . . . . . . . . . . . . . . . . . 1543.11 Variance decomposition due to the output gap . . . . . . . . . . . . . . . 1553.12 Variance decomposition due to slope factor . . . . . . . . . . . . . . . . . 1563.13 Variance decomposition due to curvature . . . . . . . . . . . . . . . . . . 1573.14 Impulse response functions to slope shock . . . . . . . . . . . . . . . . . . 158

iv

Liste des tableaux

Liste des tableaux

1.1 Eigenvalues and percent of variance of first four factors . . . . . . . . . . . 441.2 Feasible triples for a highly variable Grid . . . . . . . . . . . . . . . . . . . 621.3 Unit root tests (Sample period 1985:3 to 2006:3) . . . . . . . . . . . . . . 721.4 Unit root tests (Sample period 1985:3 to 2006:3) . . . . . . . . . . . . . . 721.5 Linearity test:VAR model . . . . . . . . . . . . . . . . . . . . . . . . . . . 731.6 MS specifications among various MS-VAR models . . . . . . . . . . . . . . 741.7 Lag length test:MSIAH-VAR model . . . . . . . . . . . . . . . . . . . . . 751.8 Transition matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 751.9 Linearity test: MS-FAVAR . . . . . . . . . . . . . . . . . . . . . . . . . . 761.10 MS specifications among various MS-FAVAR model . . . . . . . . . . . . . 771.11 Lag length test:MSIAH-FAVAR model . . . . . . . . . . . . . . . . . . . . 781.12 Transition matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

2.1 Model comparison with Deviance Information Criterion (DIC) . . . . . . . 1042.2 Feasible triples for a highly variable Grid . . . . . . . . . . . . . . . . . . . 116

3.1 Descriptive statistics : Japanese spot rate curves . . . . . . . . . . . . . . 132

v

nomenclature

vi

Nomenclature

BOJ Bank of Japan

CAB Current Account Balances of Financial Institutions held with the Bank of Japan

CPI Consumer Price Index

EH Expectation Hypothesis

EM Expectation-Maximisation

FAVAR Factor-Augmented Vector Autoregression

JGB Japanese Government Bonds

JSDA Japan Securities Dealers Association

M0 Monetary Base

MCMC Markov chain Monte Carlo

MS-VAR Markov-Switching Vector Autoregression

QEMP Quantitative easing Monetary Policy

TSE Tokyo Stock Exchange

TVP-FAVAR Time-Varying Parameter Factor-Augmented Vector Autoregression

VECM Vector Error Correction Model

ZIRP Zero Interest Rate Policy

nomenclature

viii

Introduction générale

Dans le contexte actuel de la crise financière qui se prolonge depuis octobre 2008, les princi-

pales banques centrales ont opté pour la poursuite de politiques monétaires expansionnistes

non conventionnelles. La banque du Japon a décidé récemment de mener une politique moné-

taire dite “Comprehensive Monetary Easing” qui diffère quelque peu par rapport à la politique

d’assouplissement quantitatif menée entre 2001 et 2006. La Fed, à son tour, confirme le

maintien de sa politique d’assouplissement des conditions de crédit débutée en 2009. Ces

politiques sont désormais orientées essentiellement vers la modification de la composition

des actifs des banques centrales par l’achat massif de titres à long terme dans le but de

baisser leurs rendements. Ceci aurait pour effet de réduire les rendements d’autres actifs

financiers, en apportant davantage de liquidité au système financier.

Jusque récemment la situation particulière de l’économie japonaise d’après 1990 était

considérée comme un cas isolé dans l’économie mondiale ; elle souffrait d’une longue stag-

nation et d’une forte tendance déflationniste, aggravée par la disparition des instruments de

politique monétaire dont dispose traditionnellement la banque centrale. La banque centrale

du Japon (BOJ) a donc dû mener une stratégie de politique monétaire “non conventionnelle”,

dite d’assouplissement quantitatif. La crise financière actuelle, en raison de sa similarité avec

celle du Japon des années 1990, a poussé les autorités monétaires des plus grandes banques

centrales à adopter ce même type de stratégie ; celles-ci cherchent donc aujourd’hui à tirer

partie des leçons de l’expérience japonaise. L’assouplissement quantitatif était-il efficace ?

Par quels canaux ? Dans quel délai ?

En effet, durant la récession qui suivit le dégonflement de la bulle spéculative au

1


début des années 1990, des politiques fiscales et monétaires expansionnistes furent menées

dans le but de stimuler l’économie japonaise. Cependant, jusqu’à début 2001, aucun signe

fort de reprise économique ne se fit sentir, du moins au niveau macroéconomique. Comme

montré par le graphique 1, l’économie japonaise entra en phase de dépression à partir de

1991, entrecoupée de quelques périodes de reprises, puis entra en déflation en 1998. N’ayant

pas mené une politique monétaire laxiste immédiatement après le dégonflement de la bulle

spéculative, la BOJ fut critiquée pour son manque de réactivité. En effet, elle ne réduisit

que progressivement son taux directeur, le réduisant de 6% en 1990 à 0,5% en 1995, et ne

l’a amené à un niveau proche de zéro qu’à partir de février 1999.

Sans montrer d’effet satisfaisant, ces politiques ont réduit les marges de manoeuvre

des autorités, qui furent alors contraintes d’employer des mesures expansionnistes inédites,

comme notamment l’accroissement de la dette publique en pourcentage de PIB, passée de

50% en 1991 à 120% environ en 2001, niveau le plus important parmi les pays industrialisés

(cf. graphique 2), ou encore comme la baisse des taux d’intérêt nominaux de court terme

jusqu’à leur niveau plancher à zéro.

Pour les autorités nippones, la question est de savoir comment faciliter la reprise

économique, étant donnés le poids élevé de la dette publique et la contrainte de non-

négativité des taux nominaux de court terme.

Les politiques budgétaires menées au Japon ont été considérées comme inefficaces1

et présentant le risque d’aggraver l’endettement public, d’autant plus qu’il est difficile d’éva-

luer le multiplicateur budgétaire pendant les périodes de récession, comme expliqué par Koo

(2008). Les outils ont alors été cherchés du côté de la politique monétaire qui pouvait jouer

un rôle crucial pour la reprise. De nombreux économistes ont donc recommandé à la BOJ de

renverser durablement les anticipations de déflation des agents privés en prenant un engage-

1Posen (1998) montre que l’inefficacité de la politique fiscale provient de sa mauvaise applicationet de l’insuffisance des montants consacrés par rapport aux objectifs initiaux. Selon lui, l’expériencede 1995 est l’exemple d’une politique fiscale expansionniste réussie.

2


Figure 1 – L’économie japonaise avant et après le dégonflement de la bulle spéculative

-0.10

-0.05

0.00

0.05

0.10

Eclatement de la bulle financière QEMPZIRP

Taux d'inflation (IPC)

Taux de croissance (PI)

Taux directeur

1000

2000

3000

19

80

19

83

19

86

19

89

19

92

19

95

19

98

20

01

20

04

20

07

TOPIX

QEMP : politique monétaire d’assouplissement quantitatif ; ZIRP : politique monétaire de taux d’intérêt zéro ;

TOPIX 100 : Tokyo Stock Price Index, indice de référence sur TSE (Tokyo Stock Exchange), valeurs de fin de

mois.

Source : ECOWIN, Banque du Japon.

ment crédible de laxisme, et en accroissant la base monétaire courante et future (Krugman

(2000) ; Bernanke et al. (2004) ; McCallum, 2000 ; Svensson, 2000 et 2003). A partir de

mars 2001 la BOJ a ainsi décidé de mener une politique d’assouplissement quantitatif qui

consiste en l’utilisation simultanée de trois stratégies non conventionnelles de politique mo-

nétaire : (i) un accroissement de la base monétaire en fixant un objectif quantitatif pour

les comptes courants détenus par les banques auprès de la banque centrale (cf. graphique

3) ; (ii) un engagement public à poursuivre une politique monétaire laxiste jusqu’à ce que

l’inflation, mesurée par l’indice des prix à la consommation hors produits alimentaires frais,

affiche durablement un taux nul ou positif ; (iii) un soutien de l’objectif quantitatif concer-

nant l’encours des comptes courants des banques privées par l’achat d’obligations d’Etat

(Japan Government Bond-JGB).

3


Figure 2 – Stimulus fiscal et dette publique au Japon 1990-2008

-3

-2

-1

0

1

2

3

Consommation Publique

Investissement Public

Total

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

50

100

150

dette publique en % de PIB

Source : Cabinet Office Japan et OCDE

Il est à noter que la politique d’assouplissement quantitatif était précédée d’un chan-

gement drastique du système financier afin de faire face à la crise financière déclenchée

suite au dégonflement de la bulle financière. En effet, les systèmes financier et bancaire

ont commencé à connaître de sérieuses difficultés suite à l’augmentation importante du

ratio de créances douteuses. Le graphique 4 montre que les créances douteuses détenues

par l’ensemble des banques ont atteint 5,5% du PIB en 1996 et que les pertes qui en ont

découlé représentaient plus de 2,5% du PIB dans la même année. Depuis lors, l’économie

Japonaise est entrée dans un cercle vicieux de déflation, stagnation et augmentation des

prêts non performants. De nombreuses banques ont eu des difficultés à réduire l’ampleur du

problème et ont fait faillite ; les deux plus importantes étaient Hokkaido Takushoku Bank et

Yamaichi Securities Company en 1997. En plus de ces problèmes internes, la crise asiatique

de 1997 a provoqué une baisse de l’activité japonaise (cf. graphique 1), exposant ainsi les

4


Figure 3 – Cibles sur le niveau des comptes courants des banques privées

Excess reserves

Current Account Balances

Trillon yen

Excess reserves


Required reserves

Target range

Target

Trillon yen

Excess reserves


Required reserves

Target range

Target

Trillon yen

Les autorités monétaires avaient comme cible environ 5 billions de yen à la mise en placede l’assouplissement quantitatif en mars 2001, puis 6 billions de yen du mois d’aout jusqu’àdécembre 2001. Elles l’ont environ doublée pour être dans la tranche de 10-15 billions de yen endécembre 2001, puis l’ont élevée au niveau de la tranche de 15-20 billions de yen en mars 2003(+40%) avant d’atteindre la tranche de 30-35 billions de yen en 2004 (+11%). Les réservesobligatoires durant cette période étaient de l’ordre de 5 billions de yen.Source : Banque du Japon

institutions financières japonaises à une augmentation de leurs créances douteuses et aux

pertes qui en découlent (cf. graphique 4). Simultanément, l’économie a connu un phéno-

mène dénommé par Koo (2008) "récession du bilan" par lequel les entreprises, comme les

ménages, ont vu leurs bilans se dégrader suite à la chute des cours des actifs financiers. Les

secours apportés par les autorités nippones au secteur financier, mis en place en plusieurs

étapes, faisaient partie d’une politique de déréglementation engagée en novembre 1996 afin

d’améliorer la transparence du système financier et de faciliter sa restructuration (big-bang

financier). Cargill et al. (2001) montrent que son application était particulièrement efficace

5


Figure 4 – Créances douteuses des banques japonaises et pertes dues à ces créances

3

4

5

6

7

8

0.5

1.0

1.5

2.0

2.5

1995 1997 1999 2001 2003 2005

QEMPZIRPCrise asiatique

En % du PIB

Créances douteuses par l'ensemble des banques de dépôts

Pertes bancaires dues aux créances douteuses (échelle de droite)

Source : Financial Services Agency (FSA)

dans la résolution des difficultés des banques en permettant d’évacuer de leurs bilans les

prêts non performants. Dans le même temps, la réforme institutionnelle de la BOJ, mise en

application par la nouvelle loi de 1998, a renforcé son indépendance par rapport au ministère

des finances ; permettant ainsi d’asseoir la crédibilité de la banque centrale et de favoriser

l’ancrage des anticipations des agents privés.

Un signe avant-coureur de la reprise apparaissait en novembre 2005, alors que le taux

d’inflation commençait à être positif (cf. graphique 1). La BOJ déclara en mars 2006 que

l’inflation demeurerait positive et soutenue et que les effets de la politique d’assouplissement

quantitatif commençaient à se faire sentir. Désormais, maintenir trop longtemps cette poli-

tique aurait pu mener à une inflation élevée et soutenue, étant donnée la forte expansion de

la base monétaire depuis 2001. Par conséquent, considérant qu’il était temps de mettre fin

à la stratégie d’assouplissement quantitatif, la BOJ a décidé de restaurer le taux d’intérêt

au jour le jour comme instrument de la politique monétaire.

6


La crise financière actuelle présente aux moins deux points de forte similitude avec la

crise japonaise. Elles trouvent toutes deux leur origine dans l’éclatement de bulles spécula-

tives qui ont chacune conduit à une baisse des prix, avec spirale déflationniste dans le cas du

Japon, spirale qui a été jusque là évitée par les autres pays du G7. De plus, dans les deux cas

les taux ont baissé à des niveaux proches de zéro, suite aux interventions des autorités mo-

nétaires pour gérer ces crises. Néanmoins, quelques différences sont à noter au niveau de la

réactivité des banques centrales dans la gestion de la crise et au niveau des politiques moné-

taires non conventionnelles mises en places. Tout d’abord, et comme première leçon tirée de

l’expérience japonaise, les principales banques centrales ont été plus réactives dans la baisse

des taux d’intérêt et dans la mise en place de politiques monétaires non-conventionnelles

dès que les taux d’intérêt nominaux de court terme atteignirent zéro. Cette réactivité a fait

défaut dans le cas du Japon. Le graphique 5 montre que la BOJ a mis plus de 6 ans pour

baisser les taux d’intérêt à un très faible niveau et environ 4 ans pour mener des stratégies

de politique monétaire alternatives non-conventionnelles quand les taux d’intérêt nominaux

atteignirent zéro. Quant aux autres banques centrales, spécialement la Fed, elles ont ré-

agit rapidement, en moins de deux ans elles ont baissé leurs taux directeurs à des valeurs

proches de zéro et ont appliqué des politiques non conventionnelles. Deuxièmement, à la

différence de la BOJ, la Banque d’Angleterre, la Banque Centrale Européenne et la banque

du Canada ont adopté une politique d’assouplissement quantitatif visant à atteindre une

cible quantitative de taille du bilan ainsi qu’à changer la composition du bilan, tout en ne

prenant pas d’engagement explicite à maintenir les taux directeurs à un bas niveau. Quant à

la politique de la Fed, qualifiée d’assouplissement de crédit, elle met principalement l’accent

sur le changement de la composition du bilan de la banque centrale avec un engagement

explicite à maintenir les taux à un faible niveau ; la taille du bilan n’étant qu’un objectif

accessoire. La Fed vise alors à soutenir d’une façon directe les marchés du crédit. Elle a

notamment facilité l’accès aux crédits à des secteurs choisis, en quantité supérieure à ce qui

7


Figure 5 – Réaction de la politique monétaire après l’éclatement de la bulle financièreau Japon et dans le reste des pays du G7

2

4

6

8

Eclatement

de la bulle

immobilière

- 2 ans 2 ans 4 ans 6 ans 8 ans

Taux directeur

BCE

Fed

Banque de Canada

Banque d'Angleterre

Mise en place de

l'assouplissement

quantitatif par la

banque du Japon

Mise en place de

polititques monétaires

non conventionelles

par les plus grandes

banques centrales

10 ans

(%)

BOJ

Source : BOJ, Fed, BCE, Banque du Canada et Banque d’Angleterre

serait fourni par des marchés financiers en difficulté. Reste alors à savoir si ces différentes

stratégies non conventionnelles de politique monétaire sont efficaces, par quels canaux de

transmission et à estimer le temps nécessaire. Seul le Japon, qui a connu une expérience

d’assouplissement quantitatif récente, mais depuis suffisamment d’années pour qu’elle soit

étudiée, peut nous fournir des éléments de réponse à ces questionnements.

L’assouplissement quantitatif : cadre théorique et épreuves empiriques

De nombreux et récents travaux de recherche, théoriques et empiriques, ont analysé l’éco-

nomie japonaise dans les années 1990 en essayant d’apporter des éléments de solution pour

la sortie de la crise.

L’analyse contemporaine du rôle de la politique monétaire considère habituellement

que l’instrument principal d’intervention des autorités monétaires est le taux d’intérêt à court

terme. Suite à l’article célèbre de Taylor (1993), une littérature étendue a cherché à identifier

8


la politique monétaire et ses effets dans le cadre des règles de taux d’intérêt, qui peuvent

être dérivées explicitement de la fonction objectif retenue par les autorités monétaires.

Néanmoins, pour mener des politiques expansionnistes en cas de crise, et dans le cas où

les taux d’intérêt approchent zéro, la règle de Taylor suggère des taux d’intérêt négatifs ; la

politique monétaire est donc contrainte. Tenir compte de la contrainte du plancher à zéro

des taux d’intérêt représente donc un nouveau défi pour l’approche de Taylor. La validité

de cette règle a été ravivée dans de nouvelles recherches ; développons l’apport des travaux

qui ont mis l’accent sur le rôle important des anticipations des taux d’intérêt courts futurs

et de l’inflation pour sortir de la spirale déflationniste et stimuler l’économie.

Partant de l’idée que l’économie japonaise est entrée en situation de trappe à liquidité,

le paradigme néo-Wicksellien, dominant l’analyse de la politique monétaire, suggère que la

politique monétaire peut toujours influencer l’économie via l’orientation des anticipations

concernant, à la fois, la trajectoire des taux courts futurs et l’inflation. Krugman (2000) était

le premier à recommander à la banque centrale du Japon d’adopter une nouvelle stratégie

de politique monétaire visant à influencer les anticipations des agents privés tout en prenant

garde au problème de crédibilité. Une augmentation de l’offre de monnaie n’a pas d’effet

si elle n’est pas accompagnée d’un engagement strict à ce que le surplus de liquidité soit

maintenu jusqu’à ce que les conditions de l’engagement soient remplies. Des raffinements

et précisions sont apportés par Eggertsson et Woodford (2003) qui affirment que le seul

moyen pour sortir de la situation de la trappe à liquidité est le contrôle des anticipations

des agents privés, en excluant tout effet d’une augmentation de la masse monétaire ou d’un

changement de la composition du bilan de la banque centrale. Ceci est dû à l’hypothèse de

parfaite substituabilité entre la monnaie et les actifs non monétaires quand les taux d’intérêt

approchent zéro. En effet, quand le taux nominal de court terme devient nul, si les encaisses

réelles excèdent un certain seuil (ou niveau de satiation), l’utilité marginale obtenue des

services de liquidité due à des encaisses réelles additionnelles devient nulle. Dans ce cas

9


la possibilité d’un rééquilibrage de portefeuille de l’agent privé suite à une augmentation

de la base monétaire est exclue. Un engagement crédible de la banque centrale pourrait

alors augmenter la demande globale et les prix en stimulant les dépenses courantes via trois

canaux : (i) par le maintien d’un taux d’intérêt à un niveau plus bas pour une durée plus

longue que prévue, (ii) en baissant le taux d’intérêt réel par l’augmentation de l’inflation

anticipée, et finalement (iii) par anticipation d’une augmentation des revenus futurs.

Svensson (2001) partage le scepticisme neo-Wicksellien à l’égard de l’efficacité de

l’approche quantitative et étend ce modèle à l’économie ouverte. Afin de sortir de la spirale

déflationniste, Svensson (2003) propose, à partir de ce qu’il appelle “Foolproof Way”, d’établir

pendant un certain temps un sentier cible pour le niveau des prix qui soit arrimé à un taux

d’inflation positif et de renforcer cette mesure par l’annonce d’une dévaluation de la monnaie.

Toutefois, Ito et Mishkin (2004) et Ito et Yabu (2007) suggèrent que ce type de politique

n’aura d’effet que si la BOJ intervient sur le marché des changes sans annonce préalable

d’une cible de taux de change ; éviter la confusion entre les ancres nominales de la politique

monétaire renforce la crédibilité de la banque centrale.

A l’inverse de l’approche neo-Wicksellienne, qui affirme que la politique d’assouplis-

sement quantitatif ne peut avoir d’effet que d’une manière indirecte au travers des anti-

cipations, l’approche monétariste écarte la possibilité de trappe à liquidité et suggère que

l’injection de la liquidité, via l’accroissement de la base monétaire, peut influencer l’économie

même si les taux d’intérêt approchent zéro. Sous cette approche, l’inflation est un phéno-

mène essentiellement monétaire ; les chocs monétaires se transmettent à l’économie réelle

en provoquant un ajustement du prix relatif des actifs réels et financiers (de court, moyen

et long terme) et, ainsi, un ajustement des portefeuilles des agents. Malgré la contrainte

due au taux d’intérêt zéro, l’accroissement de la base monétaire permet donc d’augmenter

la consommation via l’effet de richesse qui incite l’agent privé à faire des dépenses supplé-

mentaires, stimulant ainsi l’activité (Metzler (1995)). Cela n’est bien sûr possible que sous

10


condition d’imparfaite substituabilité entre les différents actifs et la monnaie2.

Dans la même lignée, une vue plus récente de l’approche monétariste se focalise

sur la prime de liquidité comme canal de transmission de la base monétaire à l’activité

(Yates (2004), Goodfriend (2000) et Andrés, López-Salido et Nelson (2004)). Etant donnée

l’imparfaite substituabilité et la différence qualitative en termes de liquidité entre la monnaie,

les obligations et les actions, une augmentation de la base monétaire pousse les agents privés

à réduire le niveau exigé de la prime de liquidité des actifs non liquides, diminuant ainsi leurs

rendements. Ce mécanisme de transmission a donc la vocation d’entraîner une relance de

l’activité économique non pas à travers une baisse des anticipations de taux courts futurs,

comme le suggère l’approche neo-Wicksellienne, mais par une baisse des taux d’intérêt de

long terme.

Koo (2008) développe une analyse différente, centrée sur le secteur privé : pour lui,

la crise japonaise résultait de ce qu’il appelle "récession par le bilan" suite à l’éclatement de

la bulle financière qui a laissé un grand nombre de socités privées avec un bilan déséquilibré.

Le secteur privé, ayant des dettes dépassant le montant des actifs, est alors un acteur

qui ne cherche plus à maximiser son profit, mais à minimiser sa dette. Il refuse donc de

s’octroyer de nouveaux crédits ou d’émettre de nouvelles obligations malgré les faibles taux

d’intérêt. Selon l’auteur, la situation de trappe à liquidité qu’a connue le Japon doit alors être

vue comme provenant du changement de comportement des emprunteurs et non pas des

prêteurs, auquel cas toute politique monétaire expansioniste visant à augmenter la capacité

des banques à octroyer des crédits est vouée à l’echec en raison de l’absence d’emprunteurs.

Néanmoins, il n’exclut pas le rôle important de la politique d’assouplissement quantitatif à

faciliter le désendettement et le fonctionnement des institutions financières.

L’assouplissement quantitatif, dans son application par la BOJ, n’exclut aucun des

2Metzler (1995) fait l’hypothèse que, parmi les actifs, seules les obligations sont parfaitementsubstituables à la monnaie. Les changements des taux d’intérêt de court terme, étant transitoires,n’affectent donc pas les décisions de consommation.

11


canaux de transmission possibles évoqués par les deux approches. Trois types de canaux

de transmission des effets des mesures opérationnelles de la politique d’assouplissement

quantitatif ont été mis en exergue :

1. l’engagement à maintenir des taux d’intérêt courts futurs à un niveau bas peut réduire

les taux d’intérêt de long terme et les rendements d’autres actifs financiers ;

2. l’effet de l’augmentation de la taille du bilan de la BOJ par la fourniture de réserves

excédentaires aux banques commerciales peut avoir lieu par l’intermédiaire de deux

canaux de transmission : (i) le canal de rééquilibrage des portefeuilles selon lequel les

agents privés estiment qu’ils disposent d’un excédent de liquidités qu’ils transfèrent

vers les autres actifs financiers et réels et vers la consommation ; (ii) le canal d’effet

du signal qui affecte les anticipations des cours futurs des taux d’intérêt ;

3. la modification de la composition du bilan de la BOJ par l’achat d’obligations d’Etat

en échange de réserves emploie les mêmes canaux de transmission que la mesure de

politique monétaire précédente, à savoir le rééquilibrage des portefeuilles et l’effet du

signal.

Les travaux empiriques examinant les canaux de transmission éventuels et théoriques men-

tionnés précédemment sont évidemment nombreux. Si ces études ont mis en évidence la

présence d’un changement de régime dans les mécanismes de transmission de la politique

monétaire japonaise (Fujiwara (2006), Inou et Okimoto (2008), Nakajima et al. (2009a)

et d’autres), elles restent partagées quant à son efficacité (Ugai, 2006 ) ; les résultats dé-

pendent des modèles et techniques économétriques utilisés et également des canaux de

transmission considérés.

Bernanke et al. (2004) s’intéressent à l’effet de l’assouplissement quantitatif sur les

anticipations des taux d’intérêt futurs. Les auteurs utilisent un modèle macro-finance basé

sur la structure par terme des taux d’intérêt. Ils montrent que le canal des anticipations,

12


généré par l’engagement de la BOJ, semble bien avoir eu l’effet escompté sur les taux

d’intérêt de long terme. Baba et al. (2005) et Oda et Ueda (2007), parviennent à spécifier

avec précision le canal des anticipations, et confirment la capacité d’un tel canal à influencer

la structure par terme des taux d’intérêt. Cela dit, l’examen de son effet sur l’activité

et l’inflation est absent de leurs travaux. Oda et Ueda (2007) montrent également que

l’effet du canal de rééquilibrage de portefeuille, direct par le changement de la composition

du bilan de la BOJ, ou indirect suite à l’augmentation de la base monétaire, n’a aucun

effet sur la prime de terme. Les travaux empiriques examinant l’effet de l’assouplissement

quantitatif sur les variables macroéconomiques utilisent souvent la méthodologie des modèles

vectoriels autorégressifs (VAR). Kimura, Kobayashi, Muranaga et Ugai (2003) ont montré

que l’efficacité des canaux de transmission est fortement incertaine et très faible. Leur

analyse empirique, se basant sur la méthodologie VAR avec des paramètres évolutifs (time-

varying parameters), permet de tenir compte de changements possibles de l’élasticité de

la demande de monnaie et des mécanismes de transmission quand les taux d’intérêt se

rapprochent de zéro. Aucun effet sur la production ni sur l’inflation n’a été détecté pendant

la période de l’assouplissement quantitatif.

Fujiwara (2006) estime un modèle VAR à changements de régimes markovien (MS-

VAR) sur la période 1985-2004 en utilisant trois puis quatre variables, à savoir l’indice de

prix à la consommation, la production industrielle, la base monétaire et le taux d’intérêt de

JGB à dix ans. Ce modèle présente l’avantage de détecter les ruptures structurelles sans

imposer a priori des contraintes sur les moments auxquels elles se produisent. Il montre que

la politique d’assouplissement quantitatif a un effet extrêmement faible sur l’activité et sur

les prix en l’absence du canal de transmission du taux d’intérêt. Plus récemment, Inou et

Okimoto (2008) et Nakajima et al. (2009a) aboutissent à d’autres conclusions en utilisant

des modèles différents (MS-VAR et TVP-VAR respectivement) ; ils détectent un effet positif

de l’expansion de la base monétaire sur la production pendant la période de l’assouplissement

13


quantitatif. L’effet d’une telle expansion sur l’inflation reste cependant limité.

Lors des analyses des effets de la politique monétaire réalisées dans les travaux

empiriques cités précedemment seul un petit nombre de variables macroéconomiques a été

pris en compte, ceci afin de maintenir le maximum de degrés de liberté possible. Or la

banque centrale, comme les intervenants sur les marchés financiers, exploitent un ensemble

d’information contenant un grand nombre de séries de données. Le modèle VAR traditionnel

montre ses limites parce qu’il exige une utilisation parcimonieuse du nombre de variables. Une

alternative, développée dans la littérature récente, a pour objectif d’obtenir des exercices

contrefactuels de politique économique à partir de modèles fondés sur la théorie économique,

à savoir, les modèles d’équilibre général intertemporels stochastiques (DSGE)3. Ces modèles,

de plus en plus utilisés par les banques centrales, présentent plusieurs avantages. Le premier

est lié à leur fondement microéconomique sur lequel est basée l’analyse des comportements

de l’économie à l’échelle macroéconomique. Le deuxième avantage est de placer la rationalité

individuelle des agents privés derrière le comportement global, ce qui est utile pour analyser

l’impact de la politique monétaire sur les anticipations d’agents privés ; ceci est en particulier

intéressant pour évaluer le canal des anticipations de la QEMP. Cette caractéristique permet

à ces modèle d’écarter la critique de Lucas (1976). Le troisième avantage réside dans le

caractère parcimonieux de ces modèles qui n’exigent pas une grande puissance de calcul

et rendent plus facile l’interprétation des résultats. Enfin, plusieurs travaux 4 montrent la

supériorité de ce type de modèles par rapport au modèle VAR structurel en terme de prévision.

Néanmoins, deux problèmes surgissent ; premièrement, les modèles DSGE, tout comme

les modèles VAR, emploient un nombre limité de séries macroéconomiques qui sont suppo-

sées résumer toute l’information pertinente pour l’estimation. De ce fait, ils sont également

sujets aux critiques formulées précedemment5. Deuxièmement, et de façon plus importante,

3Fernández-Villaverde (2010) fournit une revue de littérature complète et détaillée sur l’évolutiondu modèle DSGE ces dernières années.

4Smets et Wouters (2003), Del Negro et al. (2005) et Collard et Fève (2008).5Boivin et Giannoni (2007) proposent une méthode d’estimation des modèles DSGE exploitant

14


les travaux empiriques effectués sur le Japon utilisant le modèle DSGE à la Smets et Wou-

ters (2003) sont basés sur un échantillon de données ne dépassant pas 2001, omettant ainsi

la période de ZIRP et de QEMP (Sugo et Ueda (2008) et Ichiue et al. (2008)). La date

de fin d’échantillon est choisie précisemment afin d’éviter la période durant laquelle les taux

d’intérêt nominaux ont atteint leur niveau plancher à zéro, ainsi ne se confrontatant pas

au problème de non-linéarité de la règle de Taylor due à la contrainte de non-négativité des

taux d’intérêt (Eggertsson et Woodford (2003)). En effet Braun and Shioji (2006) précisent

que la présence de la contrainte de non négativité dans la règle de politique monétaire crée

deux difficultés. D’abord, cela complique la résolution du modèle étant donné que la règle

de Taylor ne peut pas être approximée par une fonction linéaire. La deuxième difficulté est

que la contrainte de non négativité des taux d’intérêt nominaux change les propriétés de

stabilité du modèle, comme précisé par Benhabib et al. (2001). Récemment, Yano (2009)

et Yano et al. (2010) tentent de résoudre le problème de non-linéarité de la règle de Taylor.

Ils utilisent la méthode de filtrage particulaire, proposée par Genshiro (1996), et estiment un

modèle DSGE de taille moyenne présenté sous forme d’un modèle espace-état non-linéaire

et non-gaussien. L’emploi de cette technique dans un modèle DSGE à la Boivin et Giannoni

(2007) utilisant un échantillon de données plus conséquent, présente une piste de recherche

future intéressante. Cette dernière raison motive l’approche choisie dans cette thèse.

Par rapport aux travaux empiriques existants sur l’assouplissement quantitatif utili-

sant essentiellement la méthodologie VAR, nous visons à utiliser une structure moins contrai-

gnante et à analyser un ensemble de données plus riche. A cet effet, nous utilisons des

modèles VAR augmentés des facteurs (FAVAR) introduits dans l’analyse de la politique mo-

nétaire par Bernanke et al. (2005) et Stock et Watson (2005). En effet, Bernanke et al.

(2005) montrent que le manque d’information dans l’analyse des modèles VAR conduit à

deux problèmes au niveau des résultats : (i) plus les informations concernant la banque

un grand nombre de variables macroéconomiques ; les auteurs montrent ainsi qu’une combinaison del’analyse factorielle et du modèle DSGE permet d’améliorer la performance de ce type de modèle.

15


centrale et le secteur privé contenues dans l’analyse sont limitées, plus la mesure des chocs

politiques est biaisée ; d’où l’apparition d’énigmes qui ont caractérisé jusque là les modèles

VAR6 (Sims (1992)) ; (ii) les fonctions de réponses obtenues pour les variables étudiées

ne permettent pas d’analyser les effets de la politique monétaire sur des concepts écono-

miques généraux comme l’activité économique ou l’investissement, qui ne peuvent pas être

représentés par une unique variable. Afin de pallier le problème de limitation du nombre de

variables, les auteurs ont développé un modèle VAR augmenté par des facteurs (FAVAR).

Les facteurs, en nombre restreint, résument un grand nombre de variables. Les résultats de

Bernanke et al. (2005) montrent que, même en utilisant un schéma d’identification récursif

à la Sims (1992), le problème des énigmes est résorbé, corroborant ainsi la thèse que les

enigmes proviennent d’insuffisance de données exploitées et non pas du schéma d’identifica-

tion (Carlstrom et al. (2009)). Ces résultats sont confirmés par Forni et Gambetti (2010)

qui montrent que, en utilisant un schéma d’identification récursif, l’emploi d’un échantillon

de données plus large produit des résultats en accord avec la thèorie économique et résout

donc le problème des énigmes. Les prix baissent immédiatement et de façon continue suite à

un choc de politique monétaire restrictif, la réaction de la production industrielle a la forme

d’une courbe en “U” reflétant la neutralité de la monnaie à long terme. Enfin, le choc de

politique monétaire impacte d’une façon importante les dynamiques des variables réelles et

nominales.

Néanmoins, bien que la méthodologie FAVAR permette d’effectuer une analyse plus

complète des mécanismes de transmission de la politique monétaire, elle ignore les change-

ments potentiels des régimes monétaires. En conséquence, l’utilisation de ce type de modèle

linéaire aboutit à des interprétations erronées des effets de la politique monétaire, spécia-

6D’autres tentatives de réconciliation des résultats empiriques issus des modèles VAR avec lathéorie, se focalisent sur la modification du schéma récursif d’identification des chocs structurels.Cela se fait en imposant soit des restrictions de court et long termes en se basant sur la théorieéconomique (Blanchard et Quah (1989) et Kim et Roubini (2000)), soit des restrictions de signe(Uhlig (2005)).

16


lement au vu des évolutions connues par l’économie japonaise durant ces deux dernières

décennies7. Dans la lignée de Sims et Zha (2006), Fujiwara (2006) et Inoue and Okimoto

(2008) utilisent la modélisation MS-VAR dans laquelle le changement de paramètres du

modèle VAR dépend des différents régimes, qui sont de nature discrète, stochastiques et

inobservables (Hamilton (1994)). Cette méthode permet non seulement de détecter les

changements de régime d’une façon endogène, mais de le faire uniquement dans le cas où

ils sont statistiquement significatifs d’une façon simultanée pour tous les paramètres ; ce qui

permet ainsi de dater les différents régimes de politique monétaire. D’autre part, les mo-

dèles VAR avec paramètres evolutifs (TVP-VAR) présentent une modélisation alternative du

changement de paramètres et fournissent plus de flexibilité, permettant aux différents para-

mètres, à savoir coefficients et volatilités, d’évoluer séparément à chaque date. Malgrè leur

différences, ces methodologies peuvent être utilisées d’une façon complémentaire. Une fois

que les régimes de politique monétaire sont détectés moyennant la méthodologie MS-VAR,

il s’avère intéressant de compléter l’analyse en utilisant la méthodologie TVP-VAR pour

détecter les évolutions des paramètres, tant permanentes que graduelles. Une extension du

modèle FAVAR a été apportée récemment par Koop et Korobilis (2009). Leur modèle non-

linéaire (TVP-FAVAR) comporte des paramètres variables dans le temps et permet donc à

la fois de tenir compte d’un maximum d’information et de détecter d’éventuelles variations

dans le temps de la relation entre les variables macroéconomiques. Bianchi et al. (2009)

ont étendu l’utilisation de la méthodologie TVP-FAVAR au modèle macro-finance appliqué

à l’analyse de la structure par terme et de sa relation avec les variables macroéconomiques.

A notre connaissance, aucune de ces méthodologies n’a encore été appliquée à l’étude de

l’assouplissement quantitatif au Japon.

7Shibamoto (2007) était le seul à employer un modèle FAVAR linéaire pour analyser la politiquemonétaire japonaise. Toutefois, son étude ne couvre pas la période de l’assouplissement quantitatif.

17


Structure de la thèse

Dans la continuité des travaux empiriques précédemment présentés, les principales contri-

butions de la présente thèse seront d’appliquer les techniques économétriques les plus ap-

propriées et les plus récentes au cas bien particulier du Japon. Ce travail gagnera en finesse

d’analyse par rapport aux tentatives précédentes en incorporant le maximum de variables

liées à la politique monétaire et en détectant avec précision les changements de régime qui

caractérisent cette dernière.

Dans un premier temps, nous analyserons les effets globaux de la stratégie d’as-

souplissement quantitatif sur l’activité et sur l’inflation. Dans un deuxième temps, nous

chercherons à discerner les canaux de transmission suggérés et à mesurer leur ampleurs à

l’aide de deux méthodologies distinctes.

Dans le premier chapitre intitulé Quantitative easing works : Lesons from the

unique experience in Japan 2001-2006 est explorée globalement l’efficacité de la politique

d’assouplissement quantitatif. A-t-elle réussi à sortir le Japon de la situation de déflation

et à stimuler son activité ? Toutefois il ne sera pas précisé par quels canaux ces effets ont

été transmis. Nous commençons par proposer un nouveau modèle, nommé MS-FAVAR,

qui combine la méthodologie de Markov-Switching et celle de FAVAR afin de tenir compte

d’éventuels changements de régimes dans la conduite de la politique monétaire japonaise. A

la différence de Bernanke et al. (2005) et suivant Belviso et Milani (2006) nous attribuons

des interprétations précises aux facteurs utilisés dans le modèle, dans la mesure où ils sont

extraits de différentes bases de données liées chacune à des notions économiques différentes.

Ces facteurs représentent l’activité économique, les prix et les taux d’intérêt. Nous montrons

à l’aide des probabilités lissées que le changement de régime s’est produit en deux étapes :

il est apparu lentement à partir de la fin de l’année 1995 et s’est installé durablement en

février 1999. Cette période est considérée comme transitoire dans l’économie japonaise

marquée par des changements drastiques au niveau du système financier (Fujiwara (2006)).

18


Nous montrons également que l’augmentation de la base monétaire pendant le deuxième

régime, qui englobe la période de politique du taux d’intérêt zéro et celle de l’assouplissement

quantitatif, a un effet positif à la fois sur la production et sur l’inflation. Bien que cet effet

soit transitoire, il montre qu’une politique monétaire passive aurait nettement aggravé la

récession : l’assouplissement quantitatif a au moins eu le mérite d’empêcher l’activité de

se détériorer davantage. Ainsi, quand la BOJ affirme que l’assouplissement quantitatif n’a

pas produit les effets désirés, il est pertinent de se demander si cette politique monétaire a

été maintenue assez longtemps. L’effet positif transitoire détecté confirme l’hypothèse que

l’assouplissement quantitatif aurait du être maintenu plus longtemps que le BOJ ne l’a fait.

Afin de pouvoir tirer davantage de leçons de l’expérience japonaise de l’assouplisse-

ment quantitatif, une analyse complémentaire s’avère être cruciale pour identifier les canaux

de transmission et mesurer leur ampleur. Le chapitre 2 , intitulé The Japanese Quanti-

tative Easing Policy under Scrutiny : A Time-Varying Parameter Factor-Augmented

VAR Model, a pour vocation de compléter le premier chapitre en détaillant les effets de la

politique d’assouplissement quantitatif sur un grand nombre de variables macroéconomiques

et financières. Dans ce chapitre nous utilisons un modèle FAVAR avec paramètres variables

dans le temps (TVP-FAVAR) pour analyser des chocs de politique monétaire au Japon. Ce

modèle présente deux avantages supplémentaires à ceux du modèle MS-FAVAR utilisé dans

le premier chapitre. Non seulement les réactions de toutes les variables sous-jacentes aux

facteurs peuvent être explorées, mais aussi, grâce à la variabilité des paramètres à chaque

période de temps, le choix de la période à étudier s’effectue d’une manière ad-hoc. Cela

nous permet donc d’analyser la période d’assouplissement quantitatif d’une manière précise.

Quatre résultats principaux se dégagent. Tout d’abord, nous montrons que le modèle où

tous les paramètres varient avec le temps est le mieux à même de spécifier la politique

monétaire japonaise pendant les deux dernières décennies. En second lieu, l’effet de l’assou-

plissement quantitatif sur l’activité et les prix est plus important que précédemment trouvé ;

19


en particulier, nous détectons, pour la premiere fois, une réaction significative des prix à un

choc sur la base monétaire. De plus, contrairement aux travaux précédents, nous montrons

que le canal de rééquilibrage de portefeuille a un rôle non négligeable dans la transmission

des chocs de la politique monétaire. Enfin, l’effet positif et significatif sur les anticipations

des agents privés de l’engagement pris par la BOJ en terme de maintien des taux d’intérêt

à des faibles niveaux, bien que transitoire, semble avoir au moins stoppé la spirale déflation-

niste. Cette dernière observation requiert une analyse supplémentaire à l’aide d’un modèle

macro-finance de la structure par terme des taux d’intérêt qui permette d’examiner avec

plus de précision les effets des anticipations.

Cette analyse fait l’objet du troisième chapitre, intitulé Quantitative Easing, Credi-

bility, and the Time-Varying Dynamic of Japan’s Term Structure, qui se concentre sur

l’interaction entre les variables macroéconomiques, dont une variable de politique monétaire,

et la structure par terme des taux d’intérêt. Nous rappelons que l’objectif intermédiaire de

la BOJ consiste à faire baisser les taux d’intérêt nominaux de long terme en ancrant, de

manière crédible, les anticipations des taux d’intérêt futurs à un niveau suffisamment bas,

niveau compatible avec une inflation modérée et stable dans le futur. Ce canal d’anticipation,

appelé canal de “policy duration effect”, n’aura d’effet que si la BOJ parvient à être crédible

dans son engagement. Cet effet aboutira, dans un deuxième temps, à une augmentation

de l’inflation anticipée et à une baisse des taux d’intérêt réels qui à son tour stimulera la

demande globale.

Dans ce chapitre nous analysons la capacité de l’assouplissement quantitatif à at-

teindre l’objectif final de la BOJ, à savoir la sortie de la déflation et la reprise de l’activité

réelle. Pour ce faire, nous employons un modèle de macro-finance à la Nelson-Siegel avec

des paramètres variables dans le temps (TVP-VAR), et utilisons l’écart de production et

l’inflation comme variables macroéconomiques, ainsi que le taux d’intérêt au jour le jour. La

structure par terme des taux d’intérêt est ainsi résumée par trois facteurs qui représentent

20


le niveau, la pente et la courbure de la courbe des taux. L’avantage de cette approche,

hormis la prise en compte des changements de régime et l’utilisation des nombreux taux

d’intérêt caractérisant la structure par terme, est qu’elle permet d’examiner à la fois l’effet

des variables macroéconomiques sur la structure par terme et l’effet de retour.

Ce chapitre débouche sur trois résultats principaux. Premièrement, nous mettons en

évidence la validité de la théorie d’anticipations rationnelles, condition nécessaire à l’effica-

cité du canal de “policy-duration effect”. L’invalidité de cette hypothèse, détectée par les

études empiriques précédentes, est généralement expliquée par la variation dans le temps de

la prime de terme qui n’est pas prise en compte par ces modèles. Deuxièmement, les résultats

des estimations de TVP-VAR montrent que les variables macroéconomiques ne contribuent

que faiblement à la variance de la structure par terme, surtout pendant la période de l’as-

souplissement quantitatif. En ce qui concerne l’effet de retour de la structure par terme sur

les variables macroéconomiques, nous détectons une contribution marginale de la courbe

des taux à la variation de l’inflation, indépendamment de la sous-période considérée ; son

effet sur la production s’avère cependant plus important. Troisièmement, en nous focalisant

sur l’effet de la politique monétaire sur la courbe des taux, nous montrons que la baisse du

facteur niveau de la courbe des taux suite à un choc positif sur le taux d’intérêt de court

terme, bien que non significative, indique que la crédibilité de la BOJ s’est renforcée pendant

la période de l’assouplissement quantitatif. Ceci est équivalent à une hausse du niveau de la

courbe des taux si on considère la politique de maintien de taux d’intérêt à un niveau bas.

Cela implique une augmentation de l’inflation anticipée et donc une éventuelle augmentation

de la demande globale. D’autre part, alors que l’effet sur la production est significatif, l’effet

sur l’inflation reste ambigu, en raison du problème d” ’énigme des prix” qui semble être lié

au nombre restreint de variables macroéconomiques considérées dans l’analyse.

21


22

11Quantitative easing works: Lessons from the

unique experience in Japan 2001-20061

1.1 Introduction

The current financial crisis has now led most major central banks to rely covertly or overtly

on quantitative easing. The unique Japanese experience of quantitative easing is the only

experience which enables us to judge this therapy’s effectiveness and determine the appropri-

1This chapter updates work registered as GREQAM working paper n 2010-2 submitted and un-der revision. We thank Stephen Bazen, Martin Ellison, Andrew Filardo, and Michel Lubrano fortheir valuable comments and suggestions. We also thank the participants of The European Doc-toral Group in Economics (EDGE) (Copenhagen, Denmark) conference, the Theory and Methodof Macroeconomics conference (Strasbourg, France) and the Day of Econometrics at University ofParis X-Nanterre, as well as the seminar participants at GREQAM (Marseille, France). This chapteralso benefited from presentations at Musashi University and Hitotsubashi University, Tokyo, July2009, and at the Bank of Japan (BOJ) in September 2009. Special thanks go to Professor YushoKagraoka and all the staff of Musashi University for their kind invitation. We are also grateful to allthe seminar participants at the BOJ for their very useful comments and suggestions and express ourspecial gratitude to Yuki Teranishi for his invitation.

23

24

ate timing of the exit strategy. It is widely believed that during the "lost" decade in Japan,

characterized both by stagnation and by deflation, monetary policy was all but impotent.

Available academic work concludes that quantitative easing, based on flooding banks with

base money, did not manage to stimulate activity or revive inflation.

The empirical study of output and price effects of monetary policy using the workhorse

in macroeconomic time series analysis, i.e. VARs (vector auto-regressive models), has been

a very intensive area of research over the last decade (Sims et al. (1990a), Sims and Zha

(1998), Bagliano and Favero (1998) and many others). Such works have usually put a lot

of emphasis on the interest rate as the monetary policy transmission channel. However,

in the case of Japan, when the zero lower bound on short-term interest rates is reached,

the room for further stimulus using a short-term interest rate instrument is constrained.

Recent researches, dealing with the issue of the zero-bound for nominal interest rates, ar-

gue that it is still possible to conduct more accommodative monetary policies to affect the

aggregate demand and prices. The neo-Wicksellian approach for monetary policy analysis

mostly focuses on alternative policies to affect expectations of future short-term interest

rates. Krugman (2000) and Eggertsson and Woodford (2003) argue that a zero interest

rate commitment influences expectations for the future path of the call rate, and then

leads to reduce medium- to long-term interest rates. However, the monetarist approach

suggests that the focus should be on portfolio-rebalancing channel. Metzler (1995) argues

that, given the imperfect substitutability of different financial assets, a massive increase in

the monetary base could lead the private sector to adjust its portfolio lowering yields on

non-monetary assets. By implementing the quantitative easing monetary policy (henceforth

QEMP), by the the Banque of Japan (BOJ) in March 2001, the monetary policy instrument

was changed to current account balances (henceforth CAB) held by commercial banks with

the BOJ. Two transmission channels for the QEMP have been suggested2. The first is the

2There are several possible ways to classify transmission channels. See also Ugai (2007)

1.1. Introduction 25

expectation channel, consisting of policy-duration (Krugman (2000) and Eggertsson and

Woodford (2003)) and signaling effects, and the second is the portfolio-rebalancing channel

(Metzler (1995)).

On the other hand, the Bank of Japan holds a large fraction of long-term bonds on its

balance sheet. About 60% of Japanese moneatry base is backed by long-term government

bonds. This measure seems to be in line with Bernanke (2003)’s recommandation. Bernanke

(2003) suggests that the BOJ dramatically increases its purshases of Japanese government

bonds. This measure would not only lead to a monetary expansion, but would also enable the

government to carry out greater fiscal stimulus without increasing the private sector’s future

tax burden. Moreover, Eggertsson (2003) argues that if government and the central bank

were to cooperate in an attempt to avoid the deflationary trap, this would create inflation

expectations in the private sector and lead to a rise in output. Therefore, Eggertsson (2003)

interprets the lack of inflation despite the large quantity of JGB issuance under zero interest

rates as evidence of lack of cooperation between Treasury officials and the central bank.

Now the policy question of major importance is to check whether results related to the

monetary policy effectiveness change when the fiscal policy is simultaneously taken into

account.

In addition, instabilities in the transmission mechanisms of monetary policy are very

likely, particularly in the case of Japan. In a standard stochastic model, Orphanides and

Wieland (2000) show that, when inflation is lower than one per cent, non-linearities in the

transmission process of monetary policy arise solely from the presence of the zero bound on

nominal interest rates. Indeed, these effects become increasingly important for determining

the outcome of monetary policy in circumstances with such low inflation rates. On an

empirical level, accounting for regime shifts should be a major concern when examining the

transmission mechanisms of monetary policy (Miyao (2000), Fujiwara (2006), Inoue and

Okimoto (2008) and Nakajima et al. (2009a)).

26

The main objective of this chapter is to asses whether the QEMP is effective in

stimulating the economy and to investigate the potential structural changes in transmission

mechanisms of Japanese monetary policy. We will therefore allow for stochastic regime

switching within a vector-autoregressive model.

Moreover, to conserve degrees of freedom, standard VARs rarely employ more than

six to eight variables. This fact is particularly important in the case of a Markov-Switching

(MS) VAR model when the number of estimated parameters rises very quickly if the number

of variables is large or the lag length is long. Moreover, in reality, policymakers work with an

information set which contains many data series. Bernanke et al. (2005) show that lack of

information in the VAR model leads to two related problems : (i) the less the central bank

and private sector related information is reflected by the analysis the more the policy shock

measure is biased. This leads to puzzles which characterize the traditional VAR model.

(ii) impulse response functions are not sufficient to analyze the effects of monetary policy

on general economic concepts like real economic activity or investment, which cannot be

represented by one variable only. Factor analysis consists in summarizing a large number of

data series to produce a small number of estimated factors. The Factor-Augmented VAR

(FAVAR) model gained in popularity with the work of Bernanke et al. (2005) and Stock

and Watson (2005). This approach attempts to reconcile traditional empirical results with

standard theory by adding further variables to the data set in the VAR system, instead of

questioning the standard recursiveness assumption of the identification scheme. Combining

Factor-Augmented and Markov Switching VAR models would enable us at the same time

to introduce a realistic amount of information, keep the statistical advantages of using a

parsimonious system, and take into account possible structural changes. We suggest this

combination could yield results more consistent with standard theory. Moreover, following

Belviso and Milani (2006), we also attribute a clear economic interpretation to the factors;

each estimated factor will represent one economic concept namely ’Real activity’, ’Inflation’


and ’Interest rates’.

The combination of these methodologies in a so-called MS-FAVAR model allows us

to establish three major findings. First, the results obtained with our model are consistent

with the standard theory and contrast sharply with those of the traditional VAR model.

Our results show that the problems of the price puzzle, the non-neutrality of money and

price divergence which characterized the MS-VAR model are solved with the MS-FAVAR.

Second, we propose new empirical evidence supporting that quantitative easing has positive

effect on both output and prices. Given the uncertainties surrounding the measurement of

output and prices during the great stagnation, using factor analysis to characterize these

two macroeconomic concepts by summarizing a large number of variables errs on the side

of caution. Third, proposing the first Markov-switching analysis of a FAVAR, we are able

to show that the decisive change in regime occurred in two steps: it crept out in late 1995

and established itself durably in 1999 around the time when the BOJ implemented QEMP.

The impulse responses in the second regime should thus describe precisely the effect of

this non-conventional strategy on output and prices. These results remain valid even when

fiscal policy is simultaneously taken into account in the analysis. However, according to the

Japanese experience, if the quantitative easing can affect the symptoms it cannot affect the

causes of the Japanese disease such as the financial distress in the banking system and the

excessive indebtness of the corporate sector.

To conduct this analysis we will proceed as follows. Section 2 discusses the related

literature. The MS-FAVAR model is described in section 3. The following two sections

examine data and estimation results and conduct a range of robustness tests. Then Section

6 develops the implications of the chapter’s main results for management by the Fed of the

global financial crisis generated by the burst of the United States housing bubble. Finally,

section 7 concludes.

28

1.2 Related literature

Conclusions on the existence and the timing of the structural changes in Japanese

monetary policy appear to be particularly sensitive to : i) the methodology employed, ii) the

variables taken into account, especially the choice of the monetary policy instrument and

iii) the period considered.

Miyao (2000) estimates a four-variable VAR. His monthly data for the call rate,

industrial production, the monetary base and the nominal effective exchange rates span

the period between 1975 and 1998. The structural change point is imposed exogenously

in 1995 by including dummy variables. Such a treatment of structural change is criticized

by Sims and Zha (2006) who argue that structural changes must be treated endogenously

where regimes are considered as stochastic events. Kamada and Sugo (2006) adopt the

VAR methodology to identify monetary policy shocks by imposing sign restrictions on the

impulse response functions. They use five variables, namely the CPI, industrial production,

the nominal exchange rate, 10-year JGB yields, and a monetary policy proxy. On the other

hand the authors use the Markov Chain Monte Carlo (MCMC) method to detect dates of

possible structural changes between February 1978 and April 2005. The detected structural

change point corresponds to the peak of the asset price bubble in 1990 and results from

a change in VAR parameters. These authors show that during the post-bubble period the

effect of monetary policy on prices and production weakened.

Fujiwara (2006) uses the Markov-switching methodology within a VAR framework

(MS-VAR) with regime-dependent impulse response functions (Ehrmann et al. (2003)). He

examines the period between 1985 and 2003 by including three and then four macroeco-

nomic variables (industrial production, CPI, the monetary base and the 10-year JGB yield).

This model represents the advantage of detecting regime changes without imposing a priori

constraints on the timing of such changes. Smoothed regime probabilities suggest that the

timing of a major regime change is most likely in 1995 and that the period between 1995

1.3. Transmission Channels of QEMP 29

and 1999 is a transition period. However, this study does not uncover any output or price

effect of monetary base shocks during the pre-1999 regime.

In the spirit of Fujiwara (2006), Inoue and Okimoto (2008) employ a MSVAR model

with five variables, namely industrial production, the consumer price index, the monetary

base, the call rate and the nominal effective exchange rate. The data span the period

between 1975 and 2002. The monetary base and the call rate both account for the monetary

policy instruments. Two regimes are identified. In the first regime the monetary policy rate

was effective until late 1995. In the second regime which started in 1996, after the interest

rate fell almost to zero, the effectiveness of interest rate shocks collapsed. However, the

monetary base in this regime has a positive and significant effect on output but a weak

effect on prices. Mehrotra (2009) uses three variables in the estimation of an MSVAR,

specifically output, the inflation rate and the call rate, as a monetary instrument. Using

data for the period between 1980 and 2003, he detects structural change in 1994. He finds

that monetary policy still has a moderate impact on output in the second regime but the

inflation response displays a price puzzle and remains insignificant.

The common point of all these studies is that a limited set of variables is used

in the analysis. In the present chapter, following the spirit of Fujiwara (2006) and Inoue

and Okimoto (2008), we treat the regime change as a stochastic event by using MSVAR

model and we combine this methodology with factor analysis. Our MS-FAVAR represents

an improvement with respect to the standard MS-VAR model since it does not suffer from

the omitted-variable bias and allows a parsimonious system.

1.3 Transmission Channels of QEMP

Several factors limited the number of monetary policy transmission channels in Japan.

First, because overnight rates have already hit the zero bound, real interest rates could only

be affected by expected inflation. Consequently monetary policy using the traditional channel

30

of the short-term interest rate was inoperative. Second, the collapse of the Japanese banking

system prevented the activation of the credit channel. Indeed, bank lending declined during

the period between 1999 and 2005 in spite of the ample liquidity provided to the banking

system (Ito and Mishkin (2006) and Ito (2006)).

The literature on monetary policy transmission when nominal short-term interest

rates hit the zero bound has focused on two transmission channels through which the QEMP

could be effective. expectation channel, which consists of policy-duration and signaling

effects, and portfolio-rebalancing channel. The expectation channel is strictly connected to

the commitment to maintain a zero interest rate until core CPI inflation becomes zero or

positive year-on-year. This channel was suggested by the neo-wicksellian approach (Krugman

(2000) and Eggertsson and Woodford (2003), to cite just a few). This approach suggests

that a credible policy commitment of maintaining nominal short-term interest rate at very

low level, for a longer period than was previously expected, can influence expectations for

the future path of the nominal rate. This, in turn decreases the long-term interest rates,

simulating aggregate demand and prices. In addition, any monetary expansion or change

in the central bank balance sheet composition is inefficient, but a permanent increase in

the monetary base can be a signal strengthening the central bank credibility of maintaining

short-term at a low level. Several empirical studies3 detect a significant effect of policy-

duration through a flattening of the yield curve. Nonetheless, more recently Nakajima et al.

(2009b) show that there is no evidence that this effect is transmitted to the real economy.

Signaling effects are suggested by all of the three courses of action included in the QEMP.

However, the most pronounced signal sent by the BOJ to the private sector is when it

purchases long-term JGB’s. In other words, the BOJ makes the commitment constraining

because it will incur a capital loss when long-term interest rates increase. Surprisingly, Oda

and Ueda (2007) detect a significant effect of this channel from the increase in CABs but

3See Oda and Ueda (2007) and Okina and Shiratsuka (2004a)

1.3. Transmission Channels of QEMP 31

no effect from the increase in the long-term JGB purchases.

On the other hand, the monetarist view argue that the potfolio-rebalancing channel

can work directly when the BOJ alters its asset composition or indirectly through the mech-

anism whereby the monetary base excess would lead the private sector to adjust its portfolio

by buying financial non-monetary assets. This channel can affect the whole spectrum of

relative asset prices and real wealth through share prices (Metzler (1995)). An increase

in money supply thus leads agents to buy equities in order to obtain the cash balances.

The increase in share prices can boost private spending through two channels, involving re-

spectively Tobin’s q-theory of investment and wealth effects. According to the former, the

increase in stock prices leads to a higher market value of firms relative to the replacement

cost of capital (the q-ratio) generating a rise in investment by firms. The latter channel

implies that the rise in financial wealth of consumers associated with higher equity prices

leads them to raise their consumption in line with the rise in their lifetime resources. An

alternative monetarist view focusing on the liquidity premium channel. This view argues

that the imperfect substitution between monetary and non-monetary assets comes from

the qualitative difference between these assets in term of liquidity. Therefore, increasing

money supply should make private sector more willing to hold other illiquid assets on their

balance sheets. The prices of these assets raise and their yields accordingly decrease (Yates

(2004)). In addition, the change in the central bank balance sheet composition by buying

long-term government bonds may reduce premia for illiquidity for these assets (Andrés et al.

(2004) and Goodfriend (2000)). In this case, the decline in long-term interest rates will be

due to the reduction in the term premiums and not to a reduction in the future nominal

short-term interest rate. From the empirical view, Kimura et al. (2003) and Oda and Ueda

(2007) show that the effect of the portfolio-rebalancing channel is insignificant or too small

considering the extensive amount of the CAB expansion and the JGB purchases.

The BOJ committed itself to maintaining this policy until inflation (measured by the

32

CPI excluding perishables) is positive and stable. It predicted in March 2006 that inflation

would remain positive and judged that the objective was reached and that it was time to exit

the QEMP. Consequently, the BOJ returned to the traditional instrument, the overnight

interest rate, as the operating target. Nevertheless, the efficacy of QEMP has not been

definitively established empirically. We suggest below to evaluate the effects of such a policy

on the real economy through the channels just cited.

1.4 Methodology

Several criticisms addressed to the VAR approach concerning the identification of

the effects of monetary policy focus on the use of a restricted quantity of information. In

order to conserve degrees of freedom, it is rare to use more than eight variables in a classical

VAR model.

Bernanke et al. (2005) show that the lack of information, from which the VAR

approach traditionally suffers, leads at least to two problems. First, taking into account only

a small number of variables in the analysis biases the measures of the monetary policy shocks.

The best illustrations of this problem are the price, interest rate, liquidity and exchange rate

puzzles. Second, the impulse response functions are observed only for variables included in

the model. The analysis thus cannot be done on global economic concepts like economic

activity or productivity, which cannot be represented by a single variable. To remedy these

problems, the authors proposed a combination between factor and VAR analysis. This

approach allows us to summarize a large amount of information in a limited number of

factors which will be used in the VAR model. Moreover, it avoids imprecision and possible

biases in the estimates that arise from the fact that any one observable may be a poor

measure of the relevant underlying concept.

However, in Bernanke et al. (2005)’s paper the factors do not have an immediate

economic interpretation. Following Belviso and Milani (2006) we provide a clear interpre-

1.4. Methodology 33

tation to these factors. We seek to identify each factor as a basic force that governs the

economy as ‘real activity’, ‘price pressure’, ‘interest rates’ and so on. We follow this litera-

ture and attempt to go a step further, seeking to take into account the possible existence of

structural change in the monetary transmission mechanism. We therefore propose a Markov

switching vector autoregression augmented with economically interpretable factors: we label

this novel approach Markov Switching Factor-Augmented VAR (MS-FAVAR).

1.4.1 MS-FAVAR

Let Xt and Yt be two vectors of economic variables, with dimensions (Nx1) and

(Mx1), where t = 1,2, ...T is a time index. Xt denotes the large dataset of economic

variables and Yt denotes the monetary policy instrument controlled by the central bank. We

assume that variables in Xt are related to a vector Ft with (Kx1) unobservable factors, as

follows :

Xt = ΛFt +et (1.1)

where et are errors with mean zero assumed to be either weakly correlated or uncorrelated;

these can be interpreted as the idiosyncratic components. The (NxK) matrix Λ represents

the factor loadings.

We can think of unobservable factors in terms of concepts such as “economic activity”

or “price pressure”. But here, following Belviso and Milani (2006) we divide Xt into various

categories X 1t , X2t , ... X

It which represent various economic concepts, where X it is a (Nix1)

vector and ∑i Ni = N. Each category of X it is thus assumed to be represented by only F it

which is a (Kix1) vector (∑i Ki = K). That means that each variable in the vector X it is

influenced by the state of the economy only through the corresponding factors. Hence we

34

obtain :

X 1t

X 2t

...

X It

=

Λf1 0 ... 0

0 Λf2 ... 0

... ... ... ...

0 0 0 ΛfI

F 1t

F 2t

...

F It

+

e1t

e2t

...

e It

(1.2)

In this analysis we assume that each segment of X it can be explained by exactly one factor,

that is Ki = 1 for all i . Also assume that the dynamics of (Yt ,F 1t ,F2t , ...,F

It ) is given by a

factor-augmented autoregression (FAVAR):

F 1t

F 2t

...

F It

Yt

=Φ(L)

F 1t−1

F 2t−1

...

F It−1

Yt−1

+νt (1.3)

A Markov-Switching FAVAR is represented by system (1.4). In its most popular

version (Krolzig (1997)), which we will use here, the regime-switching model is based on

the assumption that the process st is a first-order Markov process. Hamilton (1989), in

his original specification, assumed that a change in regime corresponds to an immediate

one-time jump in the process mean. We rather consider the possibility that the mean would

smoothly approach a new level after the transition from one regime to another. We do it in

an extension of Hamilton’s approach to a regime-switching VAR system (Krolzig (1997)).

Zt =

α1+B11Zt−1+ ... +Bp1Zt−p+A1ut if st = 1

...

αm+B1mZt−1+...+BpmZt−p+Amut if st =m

(1.4)

1.4. Methodology 35

where Zt =[F 1t F 2t ... F It Yt

]T. Each regime is characterized by an intercept αi ,

autoregressive terms B1i , ... ,Bpi and a variance-covariance matrix Ai . We assume that m,

the number of regimes, is equal to two. In this general specification all parameters are

allowed to switch between regimes according to a hidden Markov chain4. With Markov-

switching heteroscedasticity, the variance of errors can also differ between the two regimes.

After the change in regime there is thus an immediate one-time jump in the variance of

errors. This model is based on the assumption of varying processes according to the state

of the economy controlled by the unobserved variable st . Here st = 1,2 is assumed to

follow the discrete time and discrete state stochastic process of a hidden Markov chain and

governed by transition probabilities pi ,j = Pr(st+1 = j |st = i), and ∑2j=1 pij = 1∀i , j ∈ (1,2).

The conditional probabilities are collected into a transition matrix P as follows:.

p =

p11 p12

p21 p22

(1.5)

For a given parametric specification of the model, probabilities are assigned to the unob-

served regimes conditional on the available information set which constitutes an optimal

inference on the latent state of the economy. We thus obtain the probability of staying in

a given regime when starting from that regime, as well as the probability of shifting to an-

other regime. The classification of regimes and the dating algorithm used imply that every

observation in the sample is assigned to one of the two regimes. We assign an observation

to a specific regime when the smoothed probability of being in that regime is higher than

one half. The smoothed probability of being in a given regime is computed by using all the

observations in the sample.

4In the terminology of Krolzig (1997) this specification is an MSIAH(m)-VAR(p) model.

36

1.4.2 Estimation

Our MS-FAVAR approach retains the advantages of a FAVAR model over a simple

VAR. Moreover, it allows us to take into account the instability of the monetary transmission

mechanism. Factors estimated from the subset databases are the unobserved variables that,

with the policy instrument, enter the MS-VAR (equation 1.4). To estimate the factors, the

variables must be transformed to induce stationarity. By contrast the variables used in a

VAR analysis do not need to be stationary. Consequently, we estimate models using variables

in level and cumulated factors5.

In the tradition of Sims et al. (1990a), the specification of a VAR system that we

use considers variables in levels6. In the case of such VARs with polynomial functions of

time and one or more unit roots, Sims et al. (1990a) show that, independently of the

order of integration of the variables, one can get a consistent estimation of coefficients.

Moreover, as Bernanke and Mihov (1998) argue that a level specification yields consistent

estimates7 whether or not there is cointegration, but difference specification is inconsistent

if certain variables are cointegrated. Moreover, focusing on the rate of inflation would not

seem adequate when examining a period of overall price stability. Mehrotra (2009) examines

whether price- or inflation-targeting would be more adequate in the deflationary environment

experienced by the Japanese economy. As Mehrotra (2007) and (2009) argue, movements

in the price level seem to be the relevant variable of interest. In particular, when the BOJ

promised to keep its interest rate at zero until the CPI inflation stabilizes at zero percent,

such an inflation level, at zero percent, actually corresponds to a price level target. By the

inclusion of the price in level, one could argue that the BOJ has adopted an implicit price

5Examine the graphs of MS-VAR residuals to find out whether the residuals are well behaved seemsreasonable. Non-stationarity of variables therefore does not impose problem with the estimation.

6According to the unit root tests for simple variables and factors shown in tables 1.3 and 1.4 inAppendix D, all variables and cumulated factors are integrated of order one (I(1))

7Also see Hamilton (1994) who shows that estimating the VAR in level produces consistentestimates even in situations where the data are integrated or cointegrated.

1.4. Methodology 37

level target.

Despite the limitations of the quantity theory8 and the zero lower bound constraint on

nominal short-term interest rate, our model aims to capture portfolio-rebalancing and policy-

duration channel effects on aggregate demand and prices. The former channel suggests

that monetary base expansion, permanent or not, can affect nominal demand and price level

through both wealth and substitution effects on financial and real assets (Metzler (1995),

Yates (2004) and Andrés et al. (2004)). The latter channel works when the monetary

expansion is understood as permanent, reinforcing the commitment to maintaining zero

interest rates and therefore decreasing long term interest rates, which in turn increases

aggregate demand and price level. Moreover, a permanent increase in monetary policy can

affect price level if the monetary expansion is realized by purchasing government bonds, as

argued in Auerbach and Obstfeld (2005). Altogether, monetary base expansion could in the

long run increase price level and could have a temporary effect on activity level.

In this chapter we consider a two-step approach to estimating 1.2-1.4. The first

step consists in estimating the factors and factor loadings. The second step is estimating

of the MS-VAR using the factors.

1.4.2.1 Factor estimation

The main approach used for the estimation of factors consists in principal com-

ponent analysis. However, as discussed by Belviso and Milani (2006), the factors thus

estimated have unknown dynamic properties because principal components do not exploit

the dynamics of the factors or the dynamics of the idiosyncratic component. Two standard

principal approaches exploit these features to extract the static factors through dynamic prin-

8The cointegration relationship between variables was explored here. Results of the VECM modelin Appendix F show that there is no evidence supporting the existence of long-term relationshipsbetween production, price, and monetary base. This can be explained by the fact that the moneymultiplier was no longer stable after 1990 and, as mentioned by Fujiwara (2006), there is a lack ofevidence to support the presence of a M2 velocity cointegrating relationship after 1985.

38

cipal components: the static principal components method proposed by Stock and Watson

(1998a) and the Generalized Dynamic Factor Model of Forni et al. (2005) (FHLR) that is a

two-step approach based on dynamic principal components. The first approach is situated

in the time domain while the second is situated in the frequency domain. Both differ from

static principal component analysis in that they allow for a possibility of autocorrelation

between idiosyncratic components. Nonetheless, there are two main differences between

Stock and Watson’s (2002) method and that of FHLR in the way they estimate the space

spanned by the factors. First, Stock and Watson’s (2002) approach estimates the factors

using the standard principal components based on a one-sided filter of the variables. But in

the FHLR approach the common factors are estimated by exploiting information about the

degree of commonality between all variables, obtained from covariance matrices of common

and idiosyncratic components, estimated in a first step. Indeed, the variables are weighted

according to their common and idiosyncratic variances. The variables having the highest

common/idiosyncratic variance ratio (commonality) are selected. Since the weights are in-

versely proportional to the variance of the idiosyncratic components, this method provides

more efficient estimates of common factors.

For the MS-FAVAR approach employed in this chapter, static factors are estimated

by using the GDFM of Forni et al. (2005). Under the GDFM each variable can be written

as the sum of two unobservable components:

xit = χit +εit = bi1(L)f1t +bi2(L)f2t + · · ·+biq(L)fqt +εit (1.6)

where χit is the common component and εt it the idiosyncratic component; bi1(L), · · · ,biq(L)

(i =0, · · · ,s) represent the dynamic loadings of order s; f1t , · · · , fqt are the q dynamic factors.

Equation 1.6 can be written in vector notation:

xit = χit +εit = Bi(L)fqt +εit = BiFt +εit (1.7)

1.4. Methodology 39

where Ft = (f ′t , · · · , f′t−s) and Bi = Bi(L). The number of static factors is equal to r =

q(s+1).

As noted above, this approach is a two-step process9. First, it uses a frequency repre-

sentation of the time series proposed by Forni et al (2000a) to estimate the spectral density

matrices of the common part ( ∑χn (θ),−π ≤ θ < π) and of the idiosyncratic part (∑εn(θ)).

Then, the covariance matrices of common and idiosyncratic components (Γχn0 and Γεn0

respectively) are obtained by using the inverse Fourier transforms of the respective spec-

tral density matrices. Second, by using estimated covariance matrices, eigenvalues and

eigenvectors are estimated by solving the generalized principal components problem:

Γχn0Vnj = Γεn0Vnjµnj

s.t.V ′nj Γεn0Vnj = Ir

(1.8)

where the columns of the (n x r) matrix Vnj correspond to the eigenvectors and µnj is a

diagonal matrix containing the first largest eigenvalues of Γχn0 and Γεn0 on its diagonal.

The first generalized principal components are estimated as follows:

Fjnt = V

′njxnt (1.9)

The static factor loadings are defined as:

[(V ′nj Γ

Tn0)

−1]′V ′nj(Γ

χn0)

′ (1.10)

where ΓTn0 = Γχn0+Γεn0.

Fjnt are consistent estimates of the unknown common factors in equation 2.2.

9The representation theory of the dynamic factor model can be found in Forni et al. (2005)

40

1.4.2.2 MS-FAVAR estimation and identification

In the second step the model is estimated with the EM10 (Expectation–Maximization)

algorithm. Estimated factors are introduced in 1.4 instead of simple variables in a classical

MS-VAR model. Finally, the confidence intervals around the impulse responses are computed

by bootstrapping techniques.

In a Markov-switching VAR, with regime-dependence in the mean, variance and

autoregressive parameters, a large number of parameters can potentially switch between

regimes. Ehrmann et al. (2003) propose using regime-dependent impulse response func-

tions in order to trace out how fundamental disturbances affect the variables in the model,

dependent on the regime. As a result, there is a set of impulse response functions for each

regime. Such response functions are conditional on a given regime prevailing at the time

of the shock and throughout the duration of the response11. They facilitate the interpreta-

tion of switching parameters by providing a convenient way to summarize the information

contained in the autoregressive parameters, variances and covariances of each regime. This

approach combines Markov-switching and identification in a two-stage procedure of esti-

mation and identification. First, a Markov-switching unrestricted VAR model is estimated,

allowing means, intercepts, autoregressive parameters, variances and covariances to switch.

Second, in order to identify the system, restrictions can be imposed on the parameter es-

timates to derive a separate structural form for each regime, from which it is possible to

compute the regime-dependent impulse response functions.

The choice of identification assumptions is controversial and has been the subject

of numerous debates in the literature. Different sets of identification assumptions can

lead to very different conclusions in the policy debate. The differences between theoretical

prediction and empirical results are known as puzzles. A classical example is the recursive

10The estimation method, identification and impulse response are detailed in Ehrmann et al. (2003)11As shown by Ehrmann et al. (2003) regimes predicted by the transition matrix must be highly

persistent in order to have useful regime dependent impulse functions.

1.4. Methodology 41

structure VAR, initiated by Sims (1980), which fails to find evidence supporting economic

theory12.

The issue of reconciling empirical results and standard theoretical model predictions

is receiving greater attention. There are two approaches to the question ’Could puzzles

be due to an identification failure or to a deficient information set?’. The first approach

focuses on the identification scheme itself and proposes alternative identification schemes,

either using VAR models with non-recursive short- or/and long-run zero restrictions based

on theory (Kim and Roubini (2000), Blanchard and Quah (1989)13 and Clarida and Gali

(1994) among others), or using sign restriction identification methodology (Uhlig (2005)).

On the other hand, the second approach, rather than questioning the validity of the standard

recursive scheme, explains the presence of puzzles by the deficiency of information considered

in VAR models. Sims (1992) shows that adding commodity price as an additional variable

in the VAR system solves the price puzzle. The FAVAR model proposed by Bernanke et al.

(2005) and used in our work generalizes this approach by adding further variables related

to activity and financial market. This could lead all economic variables, not only prices, to

respond in accordance with theory. We therefore use a recursive structural VAR à la Sims

(1980) in order both to check the expected advantages of the fact that our model reflects

the economic theory even when a standard Cholesky scheme is adopted, and to facilitate

comparison with VAR results, particularly with Fujiwara (2006)’s MS-VAR results.

12While economic theory predicts that monetary policy has a sizeable effect on prices and pro-duction and that following a restrictive monetary policy prices decrease immediately at all horizonsand production decreases, assuming an inverted hump shape, recursive VARs generally lead to priceincrease (price puzzle) and limited and permanent decrease in production.

13Note that imposing zero long-run effect has been questioned by Faust and Leeper (1994) whoargue that in finite samples the long-run effect of shocks is imprecisely estimated and the inferencesregarding impulse responses are biased. Moreover, this methodology requires variables to enter intothe model in first differences, which can be problematic for the reasons explained in the beginning ofthe section (1.3.2).

42

1.5 Empirical Analysis

In the following, we report the results from the estimation of a MS-FAVAR model

on a data set including 3 sub-groups of factors, representing 3 economic concepts, and

a monetary policy instrument. Our vector Xt contains 135 variables. Since we focus our

empirical analysis on the quantitative easing period our sample spans the period between

1985 : 3 and 2006 : 03 at a monthly frequency. A full description of the database is provided

in appendix B. The standard method to evaluate monetary policy through a VAR model is to

consider the uncollateralized overnight call interest rate as the monetary policy instrument.

In the special case of Japan, where interest rates were almost zero, this method cannot be

applied, because interest rates contained no more information concerning monetary policy.

Theoretical work investigated alternative variables, so-called intermediate variables, which

are not directly controlled by the central bank. These variables can be the long-term interest

rate, the exchange rate, the interest rate spread and a monetary policy proxy (Kamada and

Sugo (2006)). Nevertheless, intermediate variables can be inconvenient as far as they can

react to their own shocks, thereby complicating the identification of monetary policy shocks.

In this chapter, we use the monetary base14 as the monetary policy instrument to measure

the effects of the quantitative easing policy in Japan. The monetary base thus represents

the only observed factor included in Yt .

1.5.1 Estimated Structural Factors

Since subsets of similar variables are considered to extract factors, the comovement

observed in these macroeconomic time series should be strong. A small number of factors

14The seasonally adjusted M0 was corrected for the Y2K effect related to the temporary surgein liquidity demand in December 1999 and January 2000. As argued in Juselius (2006) transitoryshocks in the model generate residual autocorrelations and violate the independence assumption ofthe VAR model. As the Y2K effect appears as an additive outlier we removed it by estimating anARMA model with transitory intervention dummies (see figure 1.6 in appendix A.)

1.5. Empirical Analysis 43

therefore account for a relevant percentage of the overall panel variance. The first obvious

check of the fit of our factor model is to see how well each factor represents each sub-group

of data series. In particular we examine the assumption according to which every sub-group

is represented by only one factor. Following Bernanke et al. (2005)and Belviso and Milani

(2006), this chapter determines the number of static and dynamic factors in an ad hoc way.

For the purpose of statistical identification, Stock and Watson (2005) estimate the number

of static and dynamic factors included in the VAR using Bai and Ng (2002)’s criterion which

determines the number of factors present in the data set. However, as Bernanke et al.

(2005) point out, Bai and Ng (2002)’s criterion, using the percentage of the variance of

the panel accounted for by common factors, describes comovements among series but does

not determine the number of factors to include in the MS-FAVAR model. In addition, the

number of parameters to estimate in the models depends on the number of variables, lags

and states and can quickly be explosive. We then extract one factor from each sub-group in

order to employ the more parsimonious system. Table 1.1 gives the results on the relative

importance of the first four factors in explaining the variance of all variables. The first factor

explains about 34, 59 and 97 percent of the data variability respectively for activity, prices,

and interest rates. Even when an additional factor is added, there is relatively little gain in

the share of variance explained. This confirms the robustness of our assumption considering

only one factor for each sub-group. Figure 1.5 in Appendix A illustrates the estimated

loadings plotted as bar charts for each factor. The numbers on the horizontal axis refer to

the ordering of the series of each subgroup and the factor loadings are on the vertical axis.

The interest rate factor loadings are high (0.6 or higher), while price and activity factor

loadings have a lower level for some variables. This is due to the fact that activity and

price variables are more heterogeneous than interest rates. Nonetheless, it appears that all

variables are involved in constructing the factors since loadings are spread across all series.

Furthermore, Figures 1.7, 1.8 and 1.9 in Appendix C show that cumulative factors clearly

44

Table 1.1: Eigenvalues and percent of variance of first four factors

Activity factors

F1a F2 F3 F4Eigenvalue 1.73 1.12 0.23 0.14

Percent variance 34.04 8.44 4.87 4.02

Price factors

Eigenvalue 2.04 0.87 0.57 .34


Interest rate factors

Eigenvalue 2.37 0.76 0.43 0.32


aFi ,(i = 1...4) denotes i –th factor.

represent the corresponding variables in level.

1.5.2 Traditional MS-VAR

We first evaluate Japanese monetary policy using the MS-VAR model following Fu-

jiwara (2006) with four observed variables namely output Yt , the price level Pt , the money

base M0t and the 10-years JGB yields BYt , but using a longer sample. Identification

achieved through a Cholesky (lower triangular) factorisation of the variance-covariance ma-

trix. The ordering Z = [Yt ,Pt ,M0t ,BYt ] implies that the measure of output, is the most

exogenous variable, the measure of price level can respond contemporaneously to output

only, whereas the instrument of monetary policy, can respond contemporaneously to both

inflation and real activity but not to the long term interest rate. The third equation in the

structural VAR is interpreted as a contemporaneous policy rule.

First and foremost, we need to determine the optimal number of regimes to charac-

terize the behavior of the time series studied. Second, the best specification among various

MS-VAR models has to be determined. We tested for linearity by taking the linear model as

the null hypothesis (there is a single regime) and the two-regime model as the alternative.


In this case the usual tests, namely LR, LM and Wald tests, cannot be conducted since the

nuisance parameter is identified only under the alternative. The problem of statistical infer-

ence when the nuisance parameters are unidentified under the null hypothesis has frequently

been addressed. Hansen (1992) and Garcia (1998) propose a non-standard likelihood ratio

test (NSLR) which is calculated as a correction on the p-value of a standard likelihood ratio

test. However, this method does not give exact critical values but only a lower bound for

the limiting distribution of a standard LR statistic and is not developed for VAR models but

for a univariate process. Since the null parameter space contains only two subsets, Cho

and White (2007) show that the NSLR test is not valid if boundary conditions are ignored.

Moreover, Cho and White (2007)’s test (QLR) is only applicable on specific models which do

not include the MSVAR. In this chapter we therefore use other tests like the Log-likelihood

or information criteria. The null hypothesis can easily be rejected as shown in Table 1.5 in

Appendix D. Moreover, the plots15 of the nonlinear model estimation residuals indicate the

absence of residual autoregression and almost all of the standardized residuals fall within

two standard deviations of a zero mean. The two-regime model is therefore supported.

Next, the best specification among various MS-VAR models has to be identified.

In this case the LR test suggested by Krolzig (1997) can be performed without causing

problems. The alternative hypothesis MSIAH-VAR specification16, where all parameters

switch between regimes, is tested against the other possible specifications. We then test

the hypothesis of no regime dependence in the variance–covariance matrix (MSIA-VAR), in

autoregressive terms (MSIH-VAR) and in both the variance–covariance matrix and autore-

gressive terms (MSI-VAR) for different lags.

The likelihood ratio test (Appendix D, Table2.1) suggests that an MSIAH-VAR model

15Plots are not reported here in order to conserve space and are available upon request fromauthors.

16According to Krolzig (1997)’s notation, MSI means that only intercepts are assumed to switchbetween regimes, MSIA means that intercepts and coefficients are assumed to switch, MSIH meansthat intercepts and variance covariance matrices are assumed to switch and MSIAH means that allparameters are assumed to switch.

46

better fits the data than other MSI-VAR specifications for two and three lags. Consequently,

this study applies the Markov switching MSIAH-VAR model in which all parameters, namely,

intercepts, autoregressive terms and variance-covariance matrices are allowed to switch

between regimes. The lag length of three is chosen in order to have serially-uncorrelated

residuals. This lag length is supported by AIC and HQ criteria (Appendix D, Table1.7).

Moreover, according to Table 1.8 in Appendix D, showing the transition matrix, the two-

regimes are highly persistent. Regime dependent impulse responses are therefore an useful

tool to analyze monetary policy of Japan.

Figure 1.1: Regime probabilities for MSIAH-VAR

1990 1995 2000 2005

0.25

0.50

0.75

1.00Smoothed prob., Regime 1

1990 1995 2000 2005

0.25

0.50

0.75

1.00Smoothed prob., Regime 2

Figure 1.1 plots smoothed regime probabilities. The Japanese economy was in regime

one up to 1997 and has been in regime two since then, with an advanced warning in 1997 and

early 1998. This result is similar to that of Fujiwara (2006); the 2000 break date coincides

neither with the beginning of ZIRP or QEMP, but lies in-between. The period between


1997 and 2000 can be interpreted as a transition period. This result confirms the choice of

non-absorbing two-state using MS-VAR model. In other words, once a state moves from

State 1 to State 2, it can return to State 1. Smoothed probabilities show that the state

evolution between these two regimes should be modeled as a transitory change. Assuming a

permanent structural change, using dummy variables or subsample analysis (Miyao (2000)),

cannot take into account reversible changes between regimes as it is the case during the

transition period.

The stylized facts on the effects of an expansionary monetary base shock were es-

tablished by Christiano et al. (1999), using impulse response functions. They conclude that

plausible models of the transmission mechanism of a monetary expansion should be consis-

tent at least with the following evidence on price, output and interest rate : i) the aggregate

price level initially responds very little, ii) output initially rises, with an inverted j-shaped re-

sponse, with a zero long-run effect of the monetary impulse, and iii) interest rates initially

fall. Figure 1.2 presents the impulse response functions to a positive shock on the monetary

base. The confidence intervals are generated using the 10th and 90th percentile values cal-

culated on the basis of 999 bootstrap replications17. Over the 1985-2000 period points i)

and iii) are almost matched, while understandably, ii) does not hold. The non-neutrality of

money and the divergence of prices after a shock on the monetary base are striking. Indeed,

output responds immediately in a persistent way, while adjustment in prices takes more than

twice as long. The 2000-2006 regime is characterized by insignificant effects of monetary

base shocks on output. The price level initially decreases insignificantly, a result known as

price puzzle18. Evaluating the reaction of long-term interest rates reveals important results.

17We refer to Davidson and MacKinnon (2000) who considered the problem of choosing thenumber of bootstrap replications.

18Carlstrom et al. (2009) argue that the price puzzle is due to the choice of the standard recursivespecification which is a wrong assumption. However, as shown in Bernanke et al. (2005) and morerecently in Forni and Gambetti (2010), price puzzle can be solved within the FAVAR approach evenwhen Cholesky identification is employed. Price puzzle therefore is due to a deficient information,including small number of variables in the VAR system, rather than to a wrong identification scheme.

48

Figure 1.2: Response to a monetary base shock in MS-VAR

regime 1 regime 2

0 1 2 3 4 5

−0.5

0.0

0.5Y

0 1 2 3 4 5

0.00

0.25Y

0 1 2 3 4 5

0.0

0.1

0.2

0.3P

0 1 2 3 4 5

0.0

0.1 P

0 1 2 3 4 5

0.5

1.0

1.5MB

0 1 2 3 4 5

0

1MB

0 1 2 3 4 5

−0.05

0.00

0.05Bond yields

0 1 2 3 4 5

−0.05

0.00

0.05Bond yields

Note: Responses of industrial production (Y), CPI (P) and 10-year JGB yields (Bond yields) toexpansionary monetary policy shock increasing the monetary base (MB) by one standard deviation.The impulse reaction period is chosen to be 5 years. Solid lines show impulse responses, whiledotted lines represent confidence intervals using the 10th and 90th percentile values calculated onthe basis of 999 bootstrap replications.

In regime one the response of the interest rate is negative but insignificant. In regime two

the reaction of bond yields is more substantial but remains insignificant. A look at the

interest-rates reaction reveals that policy-duration and signaling effects could affect prices

in the expected way, even though they remain weak. There is thus little evidence that the

transmission mechanism of Japanese monetary policy at a time of near-zero interest rates

would work essentially through the effects on the term-structure of interest rates.

The MS-FAVAR estimate results can shed some light on this question.


1.5.3 MS-FAVAR

In the following, we present the estimated effects of the QEMP within the aforemen-

tioned specifications of model 1.4. Since we identify monetary shocks by using the Cholesky

decomposition, the factor ordering must be determined carefully. The interest rate factor

includes several long-term rates that contain expectations on the economy. Because the

monetary authorities can react only to the current state of the economy, the interest rate

factor is ordered after the monetary base. We consider therefore the following ordering: real

activity factor, price factor, monetary base and the interest rate factor. Information criteria

(Appendix E, Table1.9) suggest that the model is non-linear.

From table 1.10 and table 1.11 in Appendix E, an MSIAH-FAVAR specification, in

which all parameters switch between regimes, is suggested by the LR test and the lag length

supported by two different information criteria is two. The transition matrix (Appendix E,

Table 1.12) implies that the regimes are highly persistent. As shown in Figure 1.3, the

change in regime occurred in two steps: it first appeared in May 1996 and established itself

durably in February 1999. Regime two thus corresponds precisely with the beginning of the

non-conventional monetary policy strategy namely the ZIRP consolidated by the QEMP.

A comparison of Figure 1.4 to Figure 1.2 indicates that broad patterns are roughly

similar, but there are some important differences between the two figures.

Figure 1.4 shows that, unlike in a classical MS-VAR, the stylized facts aforementioned

are verified in all points in both regimes. By contrast with Fujiwara (2006), Kamada and

Sugo (2006) and Kimura, Kobayashi, Muranaga and Ugai (2003) we detect a positive and

significant effect on real activity even in the second regime under QEMP. In the pre-1996

regime (regime 1), the response of the output factor is moderate and short lived, while

the response of the price factor is half as large, as quick, but hardly significant. Under the

second regime, after its initial rise the monetary base subsequently falls smoothly towards its

initial level; within eight months approximately half of the initial innovation has disappeared.

50

Figure 1.3: Regime probabilities for MS-FAVAR

1985 1990 1995 2000 2005

0.25

0.50

0.75

1.00

Smoothed prob., regime 1

1985 1990 1995 2000 2005

0.25

0.50

0.75

1.00

Smoothed prob., regime 2

The response of the output factor is three times as large as under the first regime, and fifty

percent longer-lived. The 90% confidence interval indicates that the effect lasts significantly

for thirteen months. The peak increase is found within 6 months. The monetary shock is

equivalent to a 1% increase in the monetary base. For reference, the total stock of CABs

was about 4.6 trillion yen at the beginning of regime two, so an increase of 1% represents

46 billion yen, leading to an increase in real activity of about 0.15% and 0.1% after six

months and one year, respectively. Moreover, the considerable successive increases in CABs

by 25%, 20%, 100% and so on, will have caused a sizable rise in Japan’s activity, respectively

of about 3.75%, 3% and 15% after six months. Granted, the magnitude and duration of

this estimated effect seem small in absolute terms and the response of output remains short-

lived. However, even though this effect becomes insignificant at the end of the first year,

it shows that a passive monetary policy would have made the recession even more severe:


Figure 1.4: Response to a monetary base shock in MS-FAVAR

0 1 2 3 4 5

0.0

0.1

0.2Regime 2

Activity

0 1 2 3 4 5

0.00

0.05

0.10Regime 1

Activity

0 1 2 3 4 5

−0.025

0.000

0.025Price

0 1 2 3 4 5

−0.025

0.000

0.025

0.050 Price

0 1 2 3 4 5

0

1

2

3 MB

0 1 2 3 4 5

0

1

2

3 MB

0 1 2 3 4 5

−0.01

0.00

Interest

0 1 2 3 4 5

−0.005

0.000

0.005 Interest

Responses of activity factor (Activity), price factor (Price) and interest rate factor (Interest) toexpansionary monetary policy shock increasing the monetary base (MB) by one standard deviation.The impulse reaction period is chosen to be 5 years. Solid lines show impulse responses, whiledotted lines represent confidence intervals using the 10th and 90th percentile values calculated onthe basis of 999 bootstrap replications.

quantitative easing must have at least prevented a further fall in output. The response of

the price factor, while slightly smaller, is much longer-lived (up to nine months) than under

the pre-1996 regime. The impulse responses indicate that a 1% increase in the monetary

base results in a cumulative 0.05% rise in prices over 5 years. Thus, when the BOJ contends

that quantitative easing did not produce the desired effects, it is at least worth considering

whether the policy was taken far enough. For example, an interesting question is whether

more positive results could have been obtained by extending the duration of quantitative

easing beyond the first signs of economic recovery in 2005, say until 2008. As argued in

52

Koo (2008), the corporate sector had just finished repaying its debts at the end of 2005,

and this would have given them time to reap the benefits.

As compared to the standard MS-VAR, it is possible to see the contribution of

the information contained in the factors and it is then noteworthy that the non-neutrality

of money, the price divergence and the price puzzle, which characterized the MS-VAR

model, disappear with the MS-FAVAR19. From the viewpoint of the liquidity premium (Yates

(2004),Andrés et al. (2004) and Goodfriend (2000)), the significance of the output effect

tends to imply that, at near-zero interest rates, base money and financial assets are not

perfect substitutes. Portfolio-rebalancing channel could therefore stimulate the economy.

In other words, an increase in the monetary base reduces the liquidity premium and leads

economic agents to adjust their portfolios away from the monetary base to financial assets,

stimulating investment. On the other hand, policy-duration and signaling effects seem to

be stronger on long-term interest rates in regime two than under regime one; the decline in

the interest rate factor becomes significant with a delay of one year. However, the positive

effect of this expectation channel remains small since the response of the interest rate factor

veers to be insignificant from the beginning of the second year.

1.5.4 Is a fiscal stimulus effective?

Most studies on the Japanese fiscal policy effectiveness during the last two decades argue

that fiscal policy was impotent or at the best would have prevented deeper depression.

Kuttner and Posen (2001) examine the hypothesis that fiscal policy was ineffective using

a VAR model. They conclude that fiscal policy was actually effective but when it is tried.

19This result confirm the view that puzzles can be solved by introducing further information in theVAR system (Bernanke et al. (2005) and Forni and Gambetti (2010), to cite aonly a few.). VARmodel with standard recursive indentification gives results consistent with standard theory when amaximum of information related to central bank and private sector is taken into account.


According to Posen (1998) the Japanese fiscal stimulus was not enough20. Guerrero and

Parker (2010), using VAR and VECM models for the period between 1955 and 2009, show

that the fiscal stimulus may have helped to prevent a more severe balance-sheet recession.

Ihori et al. (2003) argue that the Keynesian fiscal policy was not effective and thus the

effect of fiscal policies was too marginal to help macroeconomic activity recover.

However, Bernanke (2003) points out that a fiscal stimulus could be important if

the BOJ increased dramatically its purchases of Japanese government bonds. He asserts

that this measure would not only lead to an monetary expansion, but would also enable

the government to carry out greater fiscal stimulus without increasing the private sector’s

future tax burden. During the QEMP the BOJ increased its purchases of long-term bonds.

About 60% of Japanese monetary base is backed by long-term government bonds. Morever,

Eggertsson (2003) argues that if government and the central bank were to cooperate in

an attempt to avoid the deflationary trap, this would create inflation expectations in the

private sector and lead to a rise in output. But if the government and the central bank do

not cooperate and the central bank maximizes an independent objective function, inflation

expectations would not form. Therefore, Eggertsson (2003) interprets the lack of inflation

despite the large quantity of JGB issuance under zero interest rates as evidence of lack of

cooperation between Treasury officials and the central bank.

In this section we examine the effectiveness of the fiscal stimulus and we check

whether results related to the monetary policy effectiveness change when fiscal policy is

simultaneously taken into account. In order to take into account fiscal policy we introduce

JGB issues variable21 ordered first22 along with the extracted three factors representing

20The highest annual structural deficit in 1990’s was 3,8 percent of GDP in 1996. But this wascomparable to or less than the highs of the united states (3,4 percent), Germany (4 percent), France(3,6 percent) when none of these countries suffered a great a recession.

21For the reasons explained above we use monthly data as for the fiscal policy proxy. As mostof data on fiscal variables are available only at yearly and quarterly frequency we use Japanesegovernment bond (JGB) issues, which is available at monthly data, as proxy for the budget deficit.

22This ordering of variables means that shock in monetary policy, activity and price have no

54

activity, prices and interest rates. Ideally, we would like to estimate a five-variable model

including the activity factor, the price factor, the monetary base, the interest rate factor and

JGB issues. However, as explained above, in the Markov-Switching VAR model the number

of parameters to estimate can quickly be explosive when we add variables, lags or states.

For this reason instead of estimating a five-variable model we estimate two four-variable

models; a model without interest rate factor (JGBissues-activity-price-M0) to evaluate the

effect of monetary policy and fiscal policy shocks on output and prices and a model with

the interest rate factor but without monetary base (JGBissues-activity-price-interestfactor)

to asses the effect of fiscal policy shocks on the interest rate factor.

Figure 1.10 (in Appendix F) presents the JGB issues in level and in variation. It is

noteworthy that after a jump following the stimulus package of 24 April 1998 the government

bond issues tended to decrease after 2001. This date coincides with the implementation of

the quantitative easing strategy. This leads us to think that fiscal and monetary policy went

in opposite direction.

Figure 1.11 (in Appendix F) plots smoothed probabilities. The timing of regimes

does not change and still have a clear interpretation as before. Clearly from the plot, the

regime change coincides with the implementation of the ZIRP and the quantitative easing

strategy in 1999. The results of the JGBissues-activity-price-M0 model are shown in figure

1.12 and 1.13. Figure 1.12 presents the impulse response functions to a positive shock to the

monetary base (M0). It is noteworthy that the results are consistent with the main results

obtained from the model without fiscal policy variable. The results for the period 1999-2006

imply that quantitative easing is effective in helping activity recover and stimulating prices,

while the response of JGB issues is insignificant for that period. The impulse response

functions to a similar shock to JGB issues are displayed in figure 1.13. The results imply

contemporaneous effect on JGB issues. As argued in Blanchard and Perotti (2002), this delayassumption reflects the fact that in short term government may be unable to adjust its bond issuein response to changes in monetary and macroeconomic conditions.


that the effect of fiscal policy is too marginal to revive output and prices. The responses

of macroeconomic variables are insignificant in the two regimes. The results of the model

with JGBissues-activity-price-interestfactor are displayed in figure 1.14. The reactions of

activity, prices and interest rate factors are insignificant in the two regimes except a very

short-lived increase in the interest rate factor in the second regime.

These results are consistent with the main results obtained by most empirical studies

dealing with the effectiveness of Japanese fiscal policy. The failure of fiscal policy can be

due to many factors. First, as argued in Koo (2008), there is a problem with calculating the

fiscal multiplier during a recession. In other words, following a fiscal policy expansion and

starting from a situation of recession, GDP could remain steady. This can happen thanks to

the fiscal stimulus. But in this case the econometric models suggest that the fiscal multiplier

is very low or null when the fiscal stimulus could have prevented the economy from further

depression and the resultant multiplier would be huge. Second, if the fiscal stimulus is lower

than the deflationary gap the remaining headwind will tend to push the economy into a

deflationary spiral and thus will lower the measured multiplier effect of the stimulus. An

alternative interpretation, which is in connection with the last explanation, is that there

is a recurring tendency for overstatement of Japanese government fiscal packages. Posen

(1998) argues that during the period between 1990 and 1998 only the stimulus package

implemented in the second half of 1995 and the early part of 1996 was large and thus

effective. According to Posen (1998), a fiscal policy stimulus can work when it is tried.

Moreover, the combined contractionary policies of 1996 and 199723 completely offset the

positive effects of the 1995 packages.

23The 1996 budget was contractionary. Cutting government spending was one of the main targetsof fiscal reconstruction movements started by “Fiscal Restructuring Target” in 1996.

56

1.6 Robustness

Our results are based on the four variables which are arranged in order of output,

price, monetary base and long-term interest rate. To check the robustness of the reported

results, we estimated two additional types of models, price-output–monetary base–bond

yields and output–price–monetary base. The three variables model was previously estimated

by Fujiwara (2006). Neither the change in the ordering of variables (and factors) nor the

exclusion of bond yields change the dominance of the MSIAH-VAR specification. The timing

of regime change in the model price-output–monetary base–bond yields is similar to that

found in the model reported here. However, the exclusion of bond yields (and interest

rate factor) did not change the timing of regimes for both the MS-VAR and MS-FAVAR

in a significant way. The results obtained from all models are qualitatively similar to the

results presented in the previous section. Under the traditional MS-VAR all models indicate

that the output and price reactions to a positive shock on the monetary base are positive

and significant during regime one. In the MS-FAVAR the additional models confirm our

basic finding that monetary base shocks still have a positive effect on output and price even

during the second regime. Moreover, we applied the method proposed by Stock and Watson

(1998a) to estimate static factors. The results obtained from the MS-FAVAR using these

factors are very similar to those of using Forni et al. (2005)’s methodology. Thus, our basic

findings remain unaltered even if we include static factors in the estimation.

1.7 Implications and Discussion

The attempt to fight the effects of the global crisis generated by a credit boom built

around the subprime-bubble has led most major central banks to rely on quantitative easing.

The Fed, ECB, and Bank of England announced the adoption of quantitative easing in,

1.7. Implications and Discussion 57

respectively January, March and 200924,

In order to draw lessons from the unique experience of quantitative easing in Japan,

a comparison between the quantitative easing programs implemented by the Fed and the

BOJ is useful. Differences between the two experiences can be classified into two categories:

those that are related to the preconditions for implementation and those that are related to

the implementation of the quantitative easing itself.

With respect to preconditions, the Japanese experience demonstrates that quantita-

tive easing should be seen as a symptomatic treatment which stimulates activity and prices.

It was preceded by a treatment that addressed the cause of the problems of the Japanese

economy and it is note worthy that the quantitative easing policy was adopted in Japan after

a dramatic change in the financial framework dealing with financial distress. Cargill et al.

(2001) investigate changes in the Japanese financial system and the BOJ’s evolution since

the early nineties. They argue that the smooth implementation of the big-bang announce-

ments succeeded in establishing an infrastructure25 for the resolution of bank failures. These

changes in the regulatory environment were combined with a commitment of 60 trillion yen

(roughly 460 billion US$) to clean up the banks’ balance sheets, a process which had not

been completed yet for the American26 and European financial systems. In addition, the

US Treasury Department announced only 30 billion US$ to remove ‘toxic’ assets from the

24The Fed has boosted its balance sheet to US$ 2.04 trillion from US$ 946 billion in September2008. For more details see “Credit and Liquidity Programs and the Balance Sheet” on the board’spublic website at www.federalreserve.gov/monetarypolicy/bst_reportsresources.htm.

25The establishment of the Financial Supervisory Agency and the Financial Reconstruction com-mittee in June and October 1998 respectively should provide more transparent reporting of nonper-forming loans and more direct control over managing the financial crisis.

26In the case of the US, the financial crisis induced the collapse of the financial markets andparticularly the securities market, which in turn caused a decline in the capacity and willingness ofthe financial system to support lending, thus tightening credit. In this context, the financial rescueneeded to be oriented mainly towards reflating the securities market and particularly the Mortgage-Backed Securities market (MBS). As explained by Ben Bernanke, the Fed chairman, at the KansasCity Federal Reserve Symposium in Jackson Hole, the Fed increased its portfolio of mortgage-backedsecurities (MBS) in order to reduce their yields and indirectly, to reduce the yields of other assets(through the portfolio-rebalancing channel).

58

banks’ balance sheets, an intervention which now seems insufficient in view of the size of

the current crisis and the Japanese experience.

As regards the implementation itself, the BOJ and the Fed used different approaches.

One principal difference is related to the timing; it took 10 years after the bubble burst in

Japan for the authorities to take on quantitative easing, while the Fed rapidly adopted this

strategy, just one year after the USA entered into financial crisis in 2007. The Fed, therefore,

was more reactive. The second difference concerns the total amount of CABs devoted to

this strategy. After only one year the increase in the reserves held by the banks with the

Fed, roughly 8% of GDP, already exceeded the level reached by those with the BOJ during

the five years of quantitative easing, between 2001 and 2006 (6% of GDP). The BOJ also

had a commitment to a clear numerical target for inflation and a fixed 5-year timetable. In

contrast, the Fed prefered flexibility. This ruled out a clear commitment, probably reduced

uncertainty and allowed for better control of inflation expectations.

This comparison is interesting against the background of the Fed’s discussions about

exit strategies at the time. One issue involved choosing between increasing either short-

term or long-term interest rates. Assuming the Fed raised short-term rates, it would face

the decision of whether or not to reduce the excess reserves in the banking system. If the

decision were taken to reduce excess reserves, the magnitude and timing of such a reduction

would need to be considered. A similar debate occurred within the BOJ in late 2004, at the

end of the series of increases in the CABs. The BOJ chose to raise short-term rates at the

end of the quantitative program while CABs were sharply reducing prior to this.

The Japanese experience suggests that efforts to clean up the bank’s balance sheets

significantly improved the effectiveness of quantitative easing. However, this effect, although

considerable, was short-lived; it became insignificant after one year. The short duration

of this effect confirms the wisdom of the Fed’s decision to maintain quantitative easing

longer, so that being short-lived, the positive effects could be exploited. In the light of

1.8. Conclusion 59

the Japanese experience, we argue that, in addition to their fast reaction and the huge

amount of CABs employed, which may have helped relieve short-term liquidity pressures in

the financial system, the Fed was better off postponing its exit from quantitative easing.

1.8 Conclusion

Facing zero lower bound interest rates, the BOJ was an early convert to QEMP. In

this chapter we propose an FAVAR approach combined with a Markov-Switching method in

order to analyze the effectiveness of the Japanese monetary policy. We implement a two-

step approach. First, structural factors are estimated from subset databases representing

different economic concepts. Second, a Markov-switching model is estimated.

Three main conclusions can be drawn from this work. First, we show for the first time

that when the Bank of Japan began QEMP, this strategy had a positive effect on activity

and prices. However, this effect was short-lived: it lasted only one year. Our results contrast

with almost all available empirical evidence on the effects of this policy. The contrast does

not stem from our use of regime-switching analysis, but rather from our use of factor analysis

in order to account for the myriad of variables which may have been interacting under this

new monetary policy of the BOJ. The transient positive effect found, even when sizable,

bears out the hypothesis that quantitative easing needs to be maintained longer than the

BOJ did, and should be seen as a symptomatic treatment. Recession and deflation were

the symptoms and not the sources of the disease of the Japanese economy, suggesting that

QEMP needed to be coupled with the necessary restructuring of the financial system.

Second, in contrast to the MS-VAR approach, our MS-FAVAR allowed us to detect

changes in monetary policy mechanisms in a reliable way; structural change occurred in

February 1999 after a period of transition starting in May 1995. Third, we show that the

MS-FAVAR model yields results consistent with standard theory. Thus, the price puzzle, the

non-neutrality of money and the price divergence in the pre-1995 regime, which characterized

60

the MS-VAR model, disappear with the MS-FAVAR. Our findings thus confirm the idea

that exploiting a larger and more realistic information set proves a more reliable way to

model monetary policy behavior. Our conclusion is that quantitative easing, when coupled

with financial reforms, can have positive effects on the economy. However, the Japanese

experience suggests that we should not expect quantitative easing to deal with such a serious

crisis in the short term, since it needs to be applied long enough for the benefits to work

thier way through to activity and prices.

In the subsequent chapter we will investigate in detail the transmission mechanisms

of Japanese monetary policy. The Interest rate factor seems to be operative and responsible

for monetary policy influence. However, this factor can be affected both by the expectation

and the portfolio-rebalancing channels. It will therefore be interesting to determine to what

degree each factor affects every transmission channel.

1.8. Conclusion 61

Appendices

A- Factor loadings

Figure 1.5: Estimated factor loadings

Note: The loadings are spread across many series. The numbers on the horizontalaxis refer to the ordering of the series of each subgroup and correlations between thevariables and the first factors (factor loadings) are on the vertical axis.

62

Figure 1.6: The original and corrected M0

Source: Bank of JapanNote: The monetary base (M0) is corrected for the Y2K effect when the BOJ hadprovided an exceptionally large amount of funds in the market.

B- Data description

Table 1.2: Variable listData are extracted from Reuters EcoWin database. The transformation codes (T) are:1 – no transformation; 2 – first difference; 4 – logarithm; 5 – first difference oflogarithm.

N Description T

Real activity factor

1 Industrial Production Total Index 5

2 Production, Capital goods, SA, Index 5

3 Production, Ceramics, stone and clay products, SA, Index 5

4 Production, Chemicals, SA, Index 5

5 Production, Construction goods, SA, Index 5

6 Production, Consumer goods, SA, Index 5

1.8. Conclusion 63

7 Production, Domestic vehicle, total 5

8 Production, Durable consumer goods, SA, Index 5

9 Production, Fabricated metals, SA, Index 5

10 Production, Food and tobacco, SA, Index 5

11 Production, General machinery, SA, Index 5

12 Production, Iron and steel, SA, Index 5

13 Production, Manufacturing, SA, Index 5

14 Production, Mining and manufacturing, SA, Index 5

15 Production, Non-durable consumer goods, SA, Index 5

16 Production, Non-ferrous metals, SA, Index 5

17 Production, Other manufacturing, SA, Index 5

18 Production, Petroleum and coal products, SA, Index 5

19 Production, Plastic products, SA, Index 5

20 Production, Precision instruments, SA, Index 5

21 Production, Producer goods, SA, Index 5

22 Production, Pulp, paper and paper products, SA, Index 5

23 Production, Semiconductor devices, SA, Index 5

24 Production, Textiles, SA, Index 5

25 Production, Transport equipment, SA, Index 5

26 Shipments, Capital goods excl transport equipment, SA, Index 5

27 Shipments, Capital goods, SA, Index 5

28 Shipments, Construction goods, SA, Index 5

29 Shipments, Consumer goods, SA, Index 5

30 Shipments, Durable consumer goods, SA, Index 5

31 Shipments, Mining and manufacturing, Index 5

32 Shipments, Mining and manufacturing, Index 5

64

33 Shipments, Non-durable consumer goods, Index 5

34 Shipments, Producer goods total, Index 5

35 Shipments, Producer goods, for mining and manufacturing, Index 5

36 Shipments, Producer goods, for others, Index 5

37 Capacity Utilization, Operation Ratio, Fabricated metals, Index 5

38 Capacity Utilization, Operation Ratio, General machinery, Index 5

39 Capacity Utilization, Operation Ratio, Iron and steel, Index 5

40 Capacity Utilization, Operation Ratio, Machinery industry, Index 5

41 Capacity Utilization, Operation Ratio, Manufacturing excluding machinery industry, Index 5

42 Capacity Utilization, Operation Ratio, Manufacturing, Index 5

43 Capacity Utilization, Operation Ratio, Petroleum and coal products, Index 5

44 Capacity Utilization, Operation Ratio, Pulp, paper and paper products, Index 5

45 Capacity Utilization, Operation Ratio, Textiles, Index 5

46 Capacity Utilization, Operation Ratio, Petroleum chemicals products, Index 5

47 Capacity Utilization, Operation Ratio, Rubber products, Index 5

48 Capacity Utilization, Operation Ratio, Transport equipment, Index 5

49 Hours Worked, Average Per Month, Electricity, gas, heat and water 1

50 Hours Worked, Average Per Month, Manufacturing 1

51 Hours Worked, Average Per Month, Mining 1

52 Unemployment, Rate 1

53 Labour Productivity, Foodstuff and tobacco (30 employees or more), Index 5

54 Labour Productivity, Furniture (30 employees or more), Index 5

55 Labour Productivity, Manufacturing (30 employees or more), Index 5

56 Labour Productivity, Textiles (30 employees or more), Index 5

57 Employment, Overall, Total 5

58 Sales at Deapartement Stores (Total) 5

1.8. Conclusion 65

59 Wholesale Trade, Food and beverages, JPY 5

60 Wholesale Trade, Furniture and house furnishing, JPY 5

61 Wholesale Trade, General merchandise, JPY 5

62 Wholesale Trade, Machinery and equipment, JPY 5

63 Wholesale Trade, Minerals and metals, JPY 5

64 Wholesale Trade, Others, JPY 5

65 Wholesale Trade, Textiles, JPY 5

66

Wholesale Trade, Total, JPY 5

67 Housing Starts, Housing built for sale 4

68 Housing Starts, Private homes 4

69 Housing Starts, Rental homes 4

70 Housing Starts, Total 4

71 Inventory Mining and manufacturing, Index, JPY, 2000=100 5

72 Inventory Construction goods, Index, JPY, 2000=100 5

73 Inventory Capital goods, Index, JPY, 2000=100 5

74 Inventory Durable consumer goods, Index, JPY, 2000=100 5

75 Inventory Non-durable consumer goods, Index, JPY, 2000=100 5

76 Inventory Consumer goods, Index, JPY, 2000=100 5

77 Inventory Producer goods, Index 5

78 New Orders, Construction, State organizations 5

79 New Orders, Construction, Total, big 50 constructors 5

80 New Orders, Construction, Works abroad 5

81 New Orders, Construction, Works executed 5

82 New Orders, Construction, Works yet to be executed 5

83 New Orders, Machine Tools, Total demand 5

66

Price factor

84 Japan, Consumer Prices, Nationwide, All Items, General, Index, JPY, 2000=100 5

85 Japan, Consumer Prices, Industrial products,All, Index, JPY, 2000=100 5

86 Japan, Consumer Prices, Industrial products,Textile, Index, JPY, 2000=100 5

87 Japan, Consumer Prices, Electricity, gas & water charges , Index, JPY, 2000=100 5

88 Japan, Consumer Prices, Services , Index, JPY, 2000=100 5

89 Japan, Consumer Prices, Durable goods , Index, JPY, 2000=100 5

90 Japan, Consumer Prices, Non Durable goods , Index, JPY, 2000=100 5

91 Japan, Consumer Prices, Food , Index, JPY, 2000=100 5

92 Japan, Consumer Prices, Reading and Recreation , Index, JPY, 2000=100 5

93 Japan, Consumer Prices, Reading and Recreation, Recreational durables , Index, JPY,

2000=100

5

94 Japan, Consumer Prices, Reading and Recreation, Recreational goods , Index, JPY, 2000=100 5

95 Japan, Consumer Prices, Reading and Recreation, Recreational Services , Index, JPY,

2000=100

5

96 Japan, Consumer Prices, Nationwide, Clothing and Footwear, Hats and caps, Index, JPY,

2000=100

5

97 Japan, Consumer Prices, Nationwide, All Items, General excluding imputed rent, Index, JPY,

2000=100

5

98 Japan, Consumer Prices, Nationwide, Miscellaneous Goods and Services, Durable goods, Index,

JPY, 2000=100

5

99 Japan, Consumer Prices, Nationwide, Transport, Private transportation, Index, JPY, 2000=100 5

100 Japan, Consumer Prices, Nationwide, Transport, Public transportation, Index, JPY, 2000=100 5

101 Japan, Consumer Prices, Nationwide, Communication, Communication, Index, JPY, 2000=100 5

102 Japan, Corporate Goods Prices, Domestic demand products, nondurable consumer goods,

Index, JPY, 2000=100

5

103 Japan, Corporate Goods Prices, Domestic demand products, total, Index, JPY, 2000=100 5

1.8. Conclusion 67

104 Japan, Corporate Goods Prices, Domestic, capital goods, Index, JPY, 2000=100 5

105 Japan, Corporate Goods Prices, Domestic, chemicals, Index, JPY, 2000=100 5

106 Japan, Corporate Goods Prices, Domestic, consumer goods, Index, JPY, 2000=100 5

107 Japan, Corporate Goods Prices, Domestic, total, Index, JPY, 2000=100 5

108 Japan, Corporate Service Prices, All items, Index, JPY, 2000=100 5

109 Japan, Corporate Service Prices, Transportation, Index, JPY, 2000=100 5

110 Japan, Corporate Service Prices, Finance and insurance, Index, JPY, 2000=100 5

Interest rate factor

111 Call Rates, Collateralized Overnight (a)/Average(b) 1

112 Average Contracted Interest Rates on Loans and Discounts of Domestically Licensed

Banks,Stock/Short-term Loans/City Banks

1

113 Average Contracted Interest Rates on Loans and Discounts of Domestically Licensed Banks,

Stock/Short-term Loans/Regional Banks

1


Stock/Short-term Loans/Regional Banks II

1


Stock/Long-term Loans/City Banks

1


Stock/Long-term Loans/Regional Banks

1


Stock/Long-term Loans/Regional Banks II

1


Loans/Regional Banks II

1


Discounts/Shinkin Banks

1


Stock/Total/Shinkin Banks

1

121 (Discontinued)Average Interest Rates on Certificates of Deposit (New Issues)/Total (through

February 2000)

1

68

122 (Discontinued)Average Interest Rates on Certificates of Deposit (New Issues)/60 days - 89

days (through February 2000)

1

123 Japan, Interbank Rates, BBA LIBOR, 3 Month, End of Period, JPY 1

124 Japan, Interbank Rates, Collateralized Overnight, Average, JPY 1

125 Japan, Treasury Bills, Bid, 3 Month, Yield, End of Period, JPY 1

126 Japan, Prime Rates, Discounts, Regional Banks II, End of Period, JPY 1

127 Japan, Prime Rates, Discounts, Regional Banks, End of Period, JPY 1

128 Japan, Prime Rates, Discounts, Shinkin Banks, End of Period, JPY 1

129 Japan, Prime Rates, Finance Corporations, Key Lending Rates, - 5 Year, End of Period, JPY 1

130 Japan, Prime Rates, Loans, City Banks, End of Period, JPY 1

131 Japan, Prime Rates, Prime Lending Rate, Long Term, End of Period, JPY 1

132 Japan, Prime Rates, Prime Lending Rate, Short Term, End of Period, JPY 1

133 Japan - Benchmark bond - Japan 10-year Government Benchmark bond yield - Yield, average

of observations through period - Japanese yen

1

134 Government Bond Yield, 10 Year, Average 1

135 10-year interest-bearing Government Bonds 1

136 10-year Local Government Bonds 1

137 10-year Government Guaranteed Bonds 1

138 5-year interest-bearing Bank debentures 1

1.8. Conclusion 69

C-

Estim

ated

factors

Figure1.7:

Activityfactor

19

85

19

90

19

95

20

00

20

05

4.3

4.4

4.5

4.6

4.7

4.8

4.9

Lrg

dp

Lco

nst

goods

Ldurc

ongd

LM

inin

gm

anuf

Lca

puto

rch

Lca

puto

rmin

dL

caputo

rfab

mL

caputo

rman

gL

caputo

rpppp

Lca

puto

rtex

t

Lca

pit

al_goods

Lco

nsg

oods

Lm

anuf

LN

ondura

consg

dL

caputo

rccs

pL

caputo

rele

cma

Lca

puto

rgen

mL

caputo

rnfm

etL

caputo

rtra

ns

cum

fact

ivit

y

Note:

The

real

activity

factor

isestimated

from

thevector

ofallthe

83thereal

activity

related

variab

les.

The

bold

lineshow

sthecu

mulativeac

tivity

factor,while

thedo

tedlin

esrepresen

tsimplevariab

lesin

loglevelrelatedto

thereal

activity

outlined

intable2.

70

Figure1.8:

Price

factor

1985

1990

1995

2000

2005

4.2

4.4

4.6

4.8

5.0

Lcp

idur

Lcp

iser

vic

eL

cpic

om

munic

atio

nL

cpit

ransp

com

Lcp

isubgrf

Lco

rpora

te2

cum

fpri

ce

Lcp

ipro

daf

Lcp

imed

icin

esL

cpil

oge

Lcp

isubgrm

isc

Lco

rpora

te1

LC

orp

ora

te3

Note:

The

pricefactor

isestimated

from

thevector

ofallthe

27pricerelatedvariab

les.

The

bold

line

show

sthecu

mulativepricefactor,while

thedo

tedlin

esrepresen

tpricerelatedvariab

les,

expressedin

loglevel,de

scribe

din

table2.

1.8. Conclusion 71

Figure1.9:

Interest

rate

factor

Note:

The

interest

rate

factor

isestimated

from

thevector

ofallthe28

interest

rate

related

variab

les.

The

bold

lineshow

sthecu

mulativeinterest

rate

factor,w

hilethedo

tedlin

esrepresen

tsomeinterest

rate

relatedvariab

lesou

tlined

intable2.

72

D- MS-VAR estimation results

Table 1.3: Unit root tests (Sample period 1985:3 to 2006:3)

ADFa PP ERS KPSS SIC lagb DETc LS Break datesCPI -1.326d -1.541 -0.503 0.017* 1 CIP -0.361 -1.451 -0.716 4.987* 5 CM0 -0.419 -1.109 -0.298 2.089* 3 CJGB Y -1.045 -0.349 -1.013 1.398* 1 C

∆ CPI -5.016* -15.013* -2.907* 1.513* 0 C -12.58* 88:05-91:10∆ IP -6.245* -21.459* -2.716* 0.134 4 C∆ M0 -5.815* -14.491* -5.261* 0.656* 3 C -6.3280* 01:08-04:02∆ JGB Y -9.180* -12.841* -5.053* 0.145 1 C

aThe 5% critical values for the tests including a constant are -2.89 for the AugmentedDickey–Fuller (ADF) and the Phillips–Perron (PP) test, -1.95 for the Elliot, Stock and Roten-berg (ERS) test and 0.46 for the Kwiatkowski, Phillips, Schmidt and Shin (KPSS) test.Whereas the ADF test, the PP test and ERS test have the null hypothesis that the vari-able tested is nonstationary, the null hypothesis for the KPSS test is stationarity. Lee andStrazicich (2003)’s model (LS) allows for two endogenous breaks both under the null hypoth-esis of a unit root and the alternative one. The critical values of LS depends the locationof breaks. While first differences in industrial production (IP) and 10-year JBG yield appearto be stationary, those in consumer price index (CPI) and monetary base (M0) are stationaryaccording to ADF, PP and ERS tests but non-stationary according to the KPSS test. The LStest indicates that first differences in CPI and M0 are stationary with break in intercept.

bNumber of lags included in the test was chosen by the Schwarz information criterion (SIC).cThis column indicates whether a constant (C) or a trend and a constant (T) are included

in the test regression.d* The rejection of the null hypothesis at the 5% level.

Table 1.4: Unit root tests (Sample period 1985:3 to 2006:3)

ADFa PP ERS KPSS SIC lagb DETc

Price -4.576*d -13.302* -0.907* 0.34 0 CActivity -3.78* -8.783* -3.28* 0.08 4 CInt rate -9.731* -9.264* -8.396* 0.05 1 C

aPrice (Price), activity (Activity) and interest rate (Int rate)factors appear to be stationary

bNumber of lags included in the test was chosen by the Schwarzinformation criterion (SIC).

cThis column indicates whether a constant (C) or a trend and aconstant (T) are included in the test regression.

d* The rejection of the null hypothesis at the 5% level.

1.8. Conclusion 73

Table 1.5: Linearity test:VAR model

Lags IC Two regimesa single regime

Lag1AIC -20.4985 -20.2435HQ -20.3584 -20.0434SC -19.7938 -19.6435

Lag2AIC -20.4987 -20.2834HQ -20.3402 -20.0345SC -19.7058 -19.7003

Lag3AIC -20.2358 -20.1348HQ -19.8345 -19.7905SC -19.3345 -19.3104

aFour variable MSVAR with output, price level,monetary base and bond yield. All information crite-rion (values in bold font) for all number of lags supportthe presence of regime shifts.

74

Table 1.6: MS specifications among various MS-VAR models

IC MSIa(2) MSIA(2) MSIH(2) MSIAH(2)

Lag1

Log-L 2675.3495 2702.3459 2833.4954 2843.8345

Parameters 36 52 46 62

LR testb 336.97 282.9772 20.6782 -χ2(R) 38.885 18.307 26.296 -

Lag2

Log-L 2683.8454 2738.3245 2826,3455 2860.3432


LR test 353.9894 244.0374 67.9954 -χ2(R) 58.124 18.307 46.194 -

Lag3

Log-L 2686.3485 2740.3428 2846.4328 2888.2394


LR test 403.7818 295.7932 83.6132 -χ2(R) 76.778 18.307 65.171 -

aAccording to Krolzig (1997)’s notation, MSI means that only inter-cepts are assumed to switch between regimes, MSIA means that interceptsand coefficients are assumed to switch, MSIH means that intercepts andvariance covariance matrices are assumed to switch and MSIAH meansthat all the parameters are assumed to switch.

bAll the calculated values of Likelihood Ratio test, except for lag = 1,are greater than Chi2 tabulated values. All the specifications are thusoutperformed by the MSIAH.

1.8. Conclusion 75

Table 1.7: Lag length test:MSIAH-VAR model

AICa HQ SC

Lag = 1 -20.4985 -20.3584 -19.7938

Lag = 2 -20.4987 -20.3402 -19.7058

Lag = 3 -20.2358 -19.8345 -19.3345

aThe lag length supported by the IC (values

in bold font) is three.

Table 1.8: Transition matrix

Regime 1a Regime 2

Regime 1 0.9227 0.0773

Regime 2 0.0563 0.9437

aNote that pi ,j = Pr(st+1 = j |st = i)

76

E- MS-FAVAR estimation results

Table 1.9: Linearity test: MS-FAVAR

Lags IC Two regimesa Linear FAVARb

Lag1

AIC -24.2349 -23.5437

HQ -23.8645 -23.5889

SC -23.5984 -23.4787

Lag2

AIC -24.4375 -24.4048

HQ -24.1653 -24.1648

SC -23.7375 -23.7861

Lag3

AIC -24.4348 -24.3904

HQ -24.0849 -24.0394

SC -23.5103 -23.4938

aThe presence of two regimes is supported

by all the information criterion for all number of

lags except SC criteria for two lags.bThe four variables MS-FAVAR consist of

real activity, price and interest rate factors and

monetary base.

1.8. Conclusion 77

Table 1.10: MS specifications among various MS-FAVAR model

IC MSI(2) MSIA MSIH MSIAH

Lag1

Log-L 3123.5672 3145.2763 3239.2340 3254.1346


LR testa 261.1348 217.7166 29,8012 -

χ2(R) 38.885 18.307 26.296 -

Lag2

Log-L 3167.3458 3243.5745 3345.9074 3354.3409


LR test 304.2359 256.4347 57.4375 -

χ2(R) 58.124 18.307 46.194 -

Lag3

Log-L 3164.3341 3222.7817 3328.0644 3372.5083


LR test 416.3484 299.4532 88.8878 -

χ2(R) 76.778 18.307 65.171 -

aSince Likelihood Ratio statistic values are greater than Chi2 tabulated

values, the null hypothesis of linearity is rejected. MSIAH FAVAR specifi-

cation is thus supported to perform better the data.

78

Table 1.11: Lag length test:MSIAH-FAVAR model

AIC HQ SC

Lag = 1 -25.2042 -24.3240 -24.2305

Lag = 2 -25.4534 -25.3941 -24.4375

Lag = 3a -25.4649 -24.3485 -23.3458

aThis lag length is supported by only AIC.

Table 1.12: Transition matrix

Regime 1a Regime 2

Regime 1 0.9517 0.0483

Regime 2 0.0671 0.9329

aNote that pi ,j = Pr(st+1 = j |st = i)

1.8. Conclusion 79

F- Fiscal policy

Figure 1.10: The JGB issuance

1985 1990 1995 2000 2005 2010

14.5

15.0

15.5

JGB issuance in level

1985 1990 1995 2000 2005 2010

-0.01

0.00

0.01

0.02

JGB issuance in variation

Source: Bank of Japan

Note: The monthly data of JGB issuance are seasonally adjusted and

transformed in log.

80

Figure 1.11: Regimes probabilities - MS-FAVAR model

1990 1995 2000 2005

0.25

0.50

0.75

1.00

0.25

0.50

0.75

1.00Smoothed prob., regime 1

1990 1995 2000 2005

0.25

0.50

0.75

1.00Smoothed prob,. regime 2

Results from MS-FAVAR model including activity and price factors, the JGB issuance

variable and the monetary base.

1.8. Conclusion 81

Figure 1.12: Response to a monetary base shock in MS-FAVAR

0 1 2 3 4 5

-0.1

0.0

0.1

Regime 1

Fiscal policy

0 1 2 3 4 5

0.0

0.2

0.4

Regime 2

Fiscal policy

0 1 2 3 4 5

0.0

0.5Activity

0 1 2 3 4 5

-0.2

0.0

0.2 Activity

0 1 2 3 4 5

-0.005

0.000

0.005

0.010 Price

0 1 2 3 4 5

0.00

0.01

0.02Price

0 1 2 3 4 5

0

1M0

0 1 2 3 4 5

0

1

2 M0

Responses of the JGB issuance variable (Fiscal policy), the activity factor (Activity) and

the price factor (Price) to an expansionary monetary policy shock increasing the mon-

etary base (MB) by one standard deviation. The impulse reaction period is chosen to

be 5 years. Solid lines show impulse responses, while dotted lines represent confidence

intervals using the 10th and 90th percentile values calculated on the basis of 999 boot-

strap replications. The impulse reaction period is chosen to be 5 years. Solid lines show

impulse responses, while dotted lines represent confidence intervals using the 10th and

90th percentile values calculated on the basis of 999 bootstrap replications.

82

Figure 1.13: Response to a fiscal policy shock in MS-FAVAR

0 1 2 3 4 5

0.5

1.0

1.5

Regime 1

Fiscal policy

0 1 2 3 4 5

0.5

1.0

1.5

Regime 2

Fiscal policy

0 1 2 3 4 5

-0.025

0.000

0.025 Activity

0 1 2 3 4 5

-0.025

0.000

0.025Activity

0 1 2 3 4 5

-0.001

0.000

0.001 Price

0 1 2 3 4 5

-0.0010

-0.0005

0.0000

0.0005 Price

0 1 2 3 4 5

-0.0025

0.0000

0.0025

0.0050 M0

0 1 2 3 4 5

0.000

0.005M0

Responses of the activity factor (Activity), the price factor (Price), and the monetary base

(M0) to a positive shock to the JGB issuance variable (Fiscal policy) (one standard de-

viation). The impulse reaction period is chosen to be 5 years. Solid lines show impulse

responses, while dotted lines represent confidence intervals using the 10th and 90th per-

centile values calculated on the basis of 999 bootstrap replications.

1.8. Conclusion 83

Figure 1.14: Response to a fiscal policy shock in MS-FAVAR

0 1 2 3 4 5

0

2

4

Regime 1

Fiscal policy

0 1 2 3 4

0.0

0.5

1.0

Regime 2

Fiscal policy

0 1 2 3 4 5

-0.05

0.00

Activity

0 1 2 3 4

-0.025

0.000

0.025Activity

0 1 2 3 4 5

-0.0025

0.0000

0.0025 Prices

0 1 2 3 4

-0.002

-0.001

0.000

0.001 Prices

0 1 2 3 4 5

-0.05

0.00

Interest rate factor

0 1 2 3 4

-0.025

0.000

0.025Interest rate factor

Responses of the activity factor (Activity), the price factor (Price), and the interest rate

factor (interest) to a positive shock to the JGB issuance variable (Fiscal policy) (one

standard deviation). The impulse reaction period is chosen to be 5 years. Solid lines

show impulse responses, while dotted lines represent confidence intervals using the 10th

and 90th percentile values calculated on the basis of 999 bootstrap replications.

84

G- VECM estimation results

Before estimating a VAR in level, we explored the possibility of using a VECM27. We started

by testing for the number of cointegrating relationships in the system and estimating the

long run relations. Juselius (2006) recommends that the stationarity tests on any single time

series should be conducted with a chi-square-distributed likelihood ratio statistic. This should

be done within a modeled system that is restricted for rank. Juselius cautions against using

univariate tests such as the Dickey-Fuller tests, because she argues that the (non)stationarity

of a series is not independent of the rank of the error-correction terms.

Following Nielsen (2004) and Juselius (2006) we first examined the plotted28 logged

levels, except for the interest rate, and first difference of the data. There is no mean

reversion and the examination of first-differenced data suggests that there are a number

of observation-specific non-normal “outliers” effects. Therefore the specification includes

a linear trend, and a number of various appropriately specified observation-specific dummy

variables to account for outliers. Then the unrestricted VAR in levels, denoting a VAR

model in logged levels, was estimated with a restricted trend and three lags. The standard

misspecification tests showed that the residuals were not well behaved. The multivariate

normality test strongly rejected the null hypothesis of normality.

We followed a procedure for the examination and analysis of potential outliers rec-

ommended by Juselius (2006) and Nielsen (2004). An observation is considered an “outlier”

if it generates a standardized residual with an absolute value which should be larger than

3.6 given our sample size. Looking at the standardized residuals there are no outliers in the

industrial production variable. Outliers in the consumer price index are present on: 1989:04

(+3.7), 1989:11 (-3,2) and 1997:04 (+6.8). In the monetary base variable the outliers are

on: 1999:12, 2000:01, 2000:02 and 2006:04 . The government bond yields show outliers

27OxMetrics and the econometrics package CATS in Rats are used the VECM analysis.28Plots are not reported here in order to conserve space

1.8. Conclusion 85

on 1998:10 (-3.7), 1998:12 (+4.5), 2003:06 and 2003:08. Outliers which can be explained

by economic events are:

• 1989:04 : the consumption tax and the consumption Tax Law took effect from 1

April 1989. There is a dramatic change in slope of the price variable starting on this

date. We can consider this outlier as permanent. Specification considerations include

a permanent shift variable for the post 1998:04 part of the sample.

• 1997:04 : Prime Minister Hashimoto decided to increase the consumption tax from

3 to 5 percent and to put an end to temporary income tax cuts. Specification con-

siderations include an impulse dummy variable to allow for the shock caused by this

intervention.

• 1998:10 : corresponds to a sharp decline in 10-year yields generated by the Russian

crisis which led to flight to quality and pulled down the term premium. This event

seems to have been temporary and a blip dummy variable included in the short run

deterministic component is specified for this event.

• 1998:12 : the sharp increase in 10-year yields reflects an increase in the public debt;

Moody’s reduced Japan’s debt rating from its highest Aaa to Aa1 on November 17,

1998. This increase can also be explained by the announcement by the Trust Fund

Bureau that it would stop outright purchases of government bonds in December 1998.

• In 2001:9 there is a permanent shift asociated with an important decision taken by

the BOJ: a change in the guideline for monetary market operations; CAB rose from

5 to 6 trillion yen. At the same date there was an increase in outright purchases of

long-term government bonds, from 400 billion to 600 billion yen per month.

• Outliers which are present on 1999:12, 2001:01 and 2000:2 correspond to the pro-

vision of extra liquidity by the BOJ to deal with the potential Year 2000-related

86

problems. As such outliers are of opposite signs: they are considered as the transitory

effects of a shock (+4, +3 and −7).

• 2006:04 corresponds to the end of QEMP (the effect seems to start here rather than

in 2006:03).

Permanent shift dummies were restricted to the cointegrating space to allow for the

possibility that the events may have had a permanent effect. However, outliers which do

not correspond to an economic event and which seem to be due to transitory effects of

shocks or simple mistakes, are likely to be additive and should therefore not be modeled.

Following Nielsen (2004) we chose to leave the additive outliers in the data set. The

inclusion of the shift, blip and transitory dummies in an unrestricted VECM does improve

the misspecification tests significantly but the multivariate normality test still rejects the

null hypothesis of normality. This is caused by the non-normality of the residuals of the

monetary-base equation in spite of major attempts to improve the specification. In the next

step, to calculate the rank test statistics we used a simulated Bartlett test. This is because

the 95 percent fractile values are adjusted for the restriction of permanent shift dummies

included in the cointegration space (Juselius (2006)). The null hypothesis corrected for the

shift dummies suggest that the null hypothesis of at least r = 0 is accepted. Hence the rank

test statistics suggest that the system has no cointegrating relation and the four variables

do not share common trends. The same analysis was conducted for cumulated factors.

Since the factors are estimated from different subsets of variables they are not orthogonal

to each other and can be cointegrated. In spite of the ability of factor analysis to eliminate

idiosyncratic shocks and therefore outliers from simple variables we cannot detect any long

term relationship between the factors and the monetary base.

22Quantitative Easing under Scrutiny: A

Time-Varying Parameter Factor-Augmented VAR

Model

2.1 Introduction

The effectiveness of the quantitative easing monetary policy (QEMP) remains a much de-

bated issue. Since this strategy is adopted by most major central banks, namely the Fed, the

Bank of England and the European Central Bank, it is crucial to know whether this strategy

can be used as an active tool to stimulate prices and foster growth, and, if so, through

which transmission channel it works. The problem of quantifying the empirical relevance of

the different channels of transmissions through which QEMP exerts its influence on output

and prices has received wide and increasing attention in recent years. A growing body of

empirical macroeconomic literature using VAR methodology has tried to gauge the effects

87

88

of the Japanese monetary policy either in the very low interest period from 1995 or more

specifically for the QEMP period. This Japanese use of QEMP, the only experience we can

learn from, still requires exploration.

Earlier VAR studies have often been concerned with measuring monetary policy and

its macroeconomic effects. See e.g.Christiano et al. (1999), Leeper et al. (1996), and

Bernanke and Mihov (1998) for studies of the U.S., and Teruyama (2001) for the research on

the Japanese monetary policy transmission mechanisms. Moreover, many researchers have

investigated possible structural breaks which can characterize the monetary transmission

mechanisms. More particularly, in the study of Japanese monetary policy all empirical studies

are fairly consensual on the fact that examining the impact of such a policy should take into

account the instability in the transmission mechanism. Structural breaks have been treated

either exogenously, by including dummy variables or by using subsample analysis (e.g Miyao

(2000)), or endogenously, by using Markov Switching VAR (MS-VAR) (e.g. Fujiwara (2006),

Inoue and Okimoto (2008) and chapter 1 above) or Time-Varying-Parameters VAR (TVP-

VAR) model (e. g. Kimura et al. (2003), Nakajima et al. (2009a)).

Miyao (2000) estimates a recursive VAR model and concludes, by using χ2 testing

procedure, that the effect of the monetary policy weakens from 1990 onwards. On the

other hand, Kimura et al. (2003) employ a time-varying VAR model for the period between

1971-2002 and detect a structural change point in 1985 after which the inflation rate is less

responsive to an expansion in the monetary base. More recently, Fujiwara (2006), Inoue and

Okimoto (2008) and Mehrotra (2009) estimate an MS-VAR model where the regime states

are considered as stochastic events. All the parameters of the models are stochastic and

switch according to a hidden Markov chain. Both Fujiwara (2006) and Inoue and Okimoto

(2008) conclude that the monetary policy is effective until around 1995-1996, when the

call rate approaches the zero boundary and subsequently weakens. In addition, the period

between 1995 and 1996 is considered as a transition period. The only work that covers


the total period of QEMP is that of Nakajima et al. (2009a). To estimate the TVP-VAR

they use quarterly data, namely the call rate, industrial production, the consumer price index

and the monetary base, for the period between 1981 and 2008. Despite the existence of

puzzles, their findings confirm to a certain extent those of Fujiwara (2006) and Inoue and

Okimoto (2008) and show a change in the effect of monetary policy on activity and prices

when interest rates become very low.

Usually, the overall effects of QEMP are examined for a single channel or a subset

of channels1; typically, one or a subset of the following channels are considered: portfolio-

rebalancing channel; signaling effect; policy-duration effect and also exchange rate channel.

All empirical studies are relatively consensual on the fact that the portfolio-rebalancing chan-

nel does not work. Empirical studies dealing with the effectiveness of such a transmission

channel, for instance Oda and Ueda (2007) and Kimura et al. (2003), show that the effect

of a portfolio-rebalancing channel is insignificant or too small considering the huge amount

of current account balances (CABs) expansion and the Japanese Government Bond (JGB)

purchased by the Bank of Japan (BOJ). Referring to the signaling effect, Oda and Ueda

(2007) detect a significant effect of this channel from the increase in CABs but no ef-

fect from the increase in long-term JGB purchases. The empirical studies dealing with the

policy-duration effect find that it significantly lowers long-term interest rates. Among these

studies we can quote Baba et al. (2005), Oda and Ueda (2007), Okina and Shiratsuka

(2004a) and more recently Nakajima et al. (2010). The later work uses a TVP-VAR model

and shows that the significant effect of the policy-duration on the yield curve and market

expectations is not transmitted to the real economy. On the other hand, Svensson (2003)

offers what he calls a “foolproof way” of escaping from a liquidity trap. The author mostly

focuses on alternative policies in a liquidity trap to affect private-sector expectations of the

future price level via the exchange rate channel. However, Ito and Mishkin (2006) and Ito

1For more detail about the transmission channels suggested by the QEMP the reader is referred

to the paper of Ugai (2007).

90

and Yabu (2007) argue that this channel can work if the BOJ neither sterilizes the interven-

tion in the foreign exchange market ordained by the Ministry of Finance, nor announces an

exchange rate target, sending a signal that the main objective remains the price level. On

the other hand, Girardin and Lyons (2008) show some effects of this channel even though

the BOJ/MOF intervention is technically fully sterilized.

All these empirical works use models with a small number of variables either to

examine the existence of structural change or to quantify the possible transmission channels

of the QEMP. However, for the reasons explained in Bernanke et al. (2005) and Stock and

Watson (2005), using limited information can lead to a biased policy shock measurement.

In other words, when information related to the central bank and the private sector is

omitted, the measurement of the unsystematic part of monetary policy may be incorrect.

This problem can be illustrated by the “puzzles” that characterize VAR results as obtained

in most of the papers cited above. Moreover, the limited information means that transition

channels are examined separately, and hence the possible interaction between channels is

not considered. Of course, the challenge in assessing the strength of any particular channel

of monetary transmission comes from the concurrent operation of multiple channels. For

example, it is hard to tell how much of the long-term interest rate decline to attribute to

a decline in stock prices (portfolio-rebalancing channel) and how much to the reduction

in private sector expectations about the path of future short-term interest rates (policy-

duration effect). However, a complete model in which a maximum of information will be

taken into account will allow us to capture most of the structure underlying the economy

and will reliably reveal what are the mechanisms through which the QEMP could affect the

economy.

In this chapter, following Bernanke et al. (2005) and Stock and Watson (2005) we

use the factor augmented VAR (FAVAR) model in order to complement the empirical works

on Japanese monetary policy cited above, specifically with introducing further variables to


the VAR data set. To our Knowledge, only one study so far has been conducted on the

Japanese economy using the FAVAR model. Shibamoto (2007) was the first to employ

a FAVAR model on Japanese data. However, since he uses data from January 1985 to

March 2001, he does not examine the QEMP period. In addition, his results should be

interpreted with great care since, as mentioned above, examining Japanese monetary policy

without taking into account structural breaks could be misleading. In the previous chapter we

combine MS-VAR methodology and factor analysis in what we call MS-FAVAR to examine

Japanese monetary policy. The MS methodology allows us to detect discrete jumps for all

parameters simultaneously; it permits us to date breaks and assess whether a new regime

appears. Our findings on regime change timing are similar to those of Fujiwara (2006) and

of Inoue and Okimoto (2008) ; the second regime corresponds to the adoption of the Zero

Interest Rate Policy (ZIRP) and QEMP. In this chapter our objective is twofold. First, we

use TVP-VAR methodology to allow for more flexible and independent variation in FAVAR

parameters and to detect permanent and even gradual variations. Given the confirmation of

regime changes in chapter 1, to go one step further, TVP-VAR methodology allows us to

examine the evolution of Japanese monetary policy at each point in time, more particularly

inside the second regime detected in chapter 1. Therefore, we will be able to focus precisely

on the QEMP period and more reliably examine the effectiveness of this strategy. Second,

it is true that the MS-FAVAR allows us to derive impulse responses for structural factors,

since they are identified, representing clear economic concepts namely, activity , prices and

interest rates. However, we cannot examine the dynamics of all the variables explained by

the factors. Therefore, we employ here the Bayesian Markov chain Monte Carlo approach

(MCMC) to the estimation of time-varying parameters in the FAVAR model (TVP-FAVAR),

developed by Koop and Korobilis (2009). With these motivations and considerations in mind,

we aim to use this complete model in order to endogenously treat the possible structural

changes in the Japanese economy and provide a more complete and detailed analysis on

92

how monetary policy shocks in Japan affect a large range of macroeconomic time series.

After analyzing a period ranging between 1978:1 and 2008:4 we obtain four main

results. First, the best model to specify the monetary policy during the last two decades

is a model where all of parameters vary over time. This corroborates our choice of a time

varying parameters model. Second, the effect of QEMP on activity and prices is stronger

than previously found. In particular, we find a significant price reaction to a monetary

policy shock. Third, in contrast with previous work, there is a detectable effectiveness of

the portfolio-rebalancing channel, which could have a role in transmitting the monetary

policy shocks. Finally, even though the effect on expectation channel is short-lived, the

policy commitment might prevent a downward spiral of expectations but were not able to

generate an inflationary pressure to escape from the deflationary spiral and to revive the

economie.

The remainder of this chapter proceeds as follows. In section two the TVP-FAVAR

model is described. Section 3 contains the data description, specification tests and results.

Section 4 concludes.

2.2 Methodology

In the previous chapter we combined MS-VAR methodology and factor analysis in MS-FAVAR

to examine the Japanese monetary policy. MS model allows for state shifts in the FAVAR

parameters only when they are significant and permits detecting simultaneous discrete jumps

for all parameters. This model not only enabled us to know whether a significant new

monetary policy regime appeared, but also permitted to date regime changes. A second

regime appeared in February 1999, covering both ZIRP and QEMP periods. The objective

of this chapter is to complement the analysis in chapter 1 by using TVP-FAVAR model,

allowing state shifts in the FAVAR parameters at the different point of the sample and not

2.2. Methodology 93

for subsamples. By doing this, we will be able to analyse the Japanese monetary policy at

each time in the sample and especially QEMP period.

2.2.1 TVP-FAVAR model

Following Koop and Korobilis (2009), this subsection shows the econometric framework of

the TVP-FAVAR. This model is a generalization of the FAVAR model developed by Bernanke

et al. (2005) and Stock and Watson (2005). Factor dynamics are given by the following

time varying parameters FAVAR:

Yt = αt +P

∑p=1

βt,pYt−p+υt (2.1)

where Yt = [Ft Rt ]′. This means that along with the unobserved factors, Yt contains an

observable factor Rt of dimension (νx1), which represents the monetary policy instrument.

The ((K +ν)x1) vector of error terms υt is mean 0 with covariance matrix Ωt of dimension

((K+ν)x(K+ν)). However, Equation 2.1 cannot be estimated directly because the factors

are unobserved. We need, therefore, as a first step, to estimate factors using a singular

value decomposition of data. Factors, becoming observable, are included in a second step in

the equation. We assume that the Xt is (Nx1) economic variable vector can be decomposed

into a (Kx1) unobservable factor vector Ft . The unobservable factors are reflected in a wide

range of economic variables. We can think of unobservable factors in terms of concepts such

as “economic activity” or “price pressures”. Assume that Xt are related to the unobservable

factors Ft and the observable factors Rt with drifting parameters, as follows :

Xt = ΛftFt +ΛRt Rt +et (2.2)

where et are errors with mean zero and variance-covariance matrixΨ= diag(exp(ψ1,t), · · · ,exp(ψn,t)).

The term error et are assumed to be either weakly correlated or uncorrelated; these can be

94

interpreted as the idiosyncratic components. Λf and ΛR are the (NxK), (Nxν) matrices of

factor loadings. The implication of the diagonality of the covariance matrix is that the pa-

rameters in equation (2.2) can be estimated equation-by-equation. This approach is needed

for reasons that will be explained below.

A Choleski decomposition of the reduced form covariance matrix Ωt can be used to

orthogonalize the reduced form innovations and to identify the structural model:

Ωt = A−1t Ht

(A−1t

)′(2.3)

The time-varying matrices Ht and At are defined as follows:

Ht ≡

h1,t 0 · · · 0

0 h2,t · · · 0

... · · ·. . .

...

0 0 · · · h(K+ν),t

(2.4)

At ≡

1 0 · · · 0

a21,t 1. . .

...

... · · ·. . . 0

a(K+ν)1,t. . . a(K+ν)k,t 1

(2.5)

As suggested by Primiceri (2005) and Koop and Korobilis (2009) we assume that all

the parameters evolve as random walks2 augmented with the mixture innovation specification

of Giordani and Kohn (2008). Therefore, the innovations of the random walk evolution of

2As explained in Primiceri (2005) the random walk assumption has the advantages of focusing

on permanent shifts and reducing the number of parameters in the estimation procedure. However,

a random walk model is non-stationary and it is obviously "more explosive" than the number of

observation increases. By choosing quarterly data for the period between 1978 Q1 and 2008 Q4 our

sample contains no more than 120 time series observations. Using such a short period alleviates this

problem.

2.2. Methodology 95

the parameters is defined as a mixture of two normal components (see koop et al 2009 and

Koop and Korobilis (2009)):

Λt = Λt−1+Jλi ,tη

λt

ψi ,t = ψi ,t−1+Jψi ,tη

ψt

φt = φt−1+Jφi ,tη

φt

at = at−1+Jai ,tηat

lnhi ,t = lnhi ,t−1+Jhi ,tηht

(2.6)

where φ = [αt βt,p] and hi ,t evolve as geometric random walks and we assume that the

innovation vectors are independent from each other and are distributed as

ηλt

ηψt

ηφt

ηat

ηht

∼ N(0,Q), where Q =

Qηλt0 · · · · · · 0

0 Qηψt

. . .. . .

...

.... . . Q

ηφt

. . ....

.... . .

. . . Qηat 0

0 · · · · · · 0 Qηht

(2.7)

The error terms in equation (3.12) are allowed, to some extent, to be mutually correlated.

However, we assume for parsimony that all error components in equations (1.1)-(1.8) are

uncorrelated with each other.

Note that the monetary policy variables are ordered last in the FAVAR (equation

(2.1)). Then by imposing some normalization as in (3.13) the unobservable factors do

not respond to the monetary policy shocks contemporaneously, and the innovations in the

equations of Rt are treated as the monetary policy shocks.

Suppose that Jt are binary random variables that control structural breaks in the

respective error term of the time varying parameters. As in Koop and Korobilis (2009) we

96

assume that Jt ∼ Bernoulli(π), where π is the probability3 corresponding to each of the

parameter vectors Λ, ψ, φ, a and lnh. Therefore, if Jt = 0 or Jt = 1 that means that

the data indicated constant and time varying parameters specifications, respectively, for all

(t = 1, ...,T ). Otherwise, data can also determine a time varying parameters specification

for some subsamples only; Jt =1 for some t. The choice of either specification is motivated

by the Bayesian procedure selection model based on marginal likelihoods. Following Koop

and Korobilis (2009), we choose the more flexible model allowing Jλt to be different for

each row of λ in equation (2.2) such that Jλit 6= Jλjt . This is the reason why equation (2.2)

is estimated equation-by-equation. We assume also that hyperparameters Qηat are block

diagonal in which each block corresponds to parameters belonging to separate equations4.

A particular advantage of the factor-augmented framework is that we can derive

impulse responses not only for the fundamental factors, but also for all the variables included

in the factors. We provide impulse responses to a monetary policy shock for some of the

most interesting variables. Equation (2.1) can be written as

Γ(L)Yt = γt (2.8)

where L is a lag operator of order p, Γ the coefficient matrix including α, Yt =[Ft Rt

]′

and γt is a ((K +ν)x1) vector of structural innovations. As the estimator of Xt using (2.2)

is Xt = Λft Ft +ΛRt Rt , impulse response functions of Xt are obtained as follows:

Xt =[Λft ΛRt

]Ft

Rt

=

[Λft ΛRt

]ζ(L)γt (2.9)

where ζ(L) =(Γ(L)

)−1.

3Also we assume that (π) is distributed as a Beta(τ0,τ1) and all probabilities have the same prior

values (τ0 = τ1) and they are common for all parameters.4We have then (K + ν) − 1 blocks, namely ablock1 = a21,t, a

block1 = a31,t ,a32,t, ...,

ablock((K+ν)−1) =a(K+ν)1,t , ...,a(K+ν)k,t

2.2. Methodology 97

2.2.2 Estimation

This section gives an overview of the estimation strategy and the algorithm used

in estimation. The Bayesian methods described by Kim and Roubini (2000) is used to

estimate the model in equations (2.1)-(3.12) for two reasons. First, if the variance of the

time varying coefficients is small, then the maximum likelihood estimator (MLE) is biased

towards a constant coefficients FAVAR. As a consequence, numerical optimization methods

are very likely to get stuck in uninteresting regions of the likelihood (Stock and Watson

(1996)). Second, multiple peaks are highly probable in a non-linear FAVAR model with

highly dimensional parameters. This makes maximum likelihood estimation quite unreliable

if in fact a peak is reached at all. Therefore, the Gibbs sampler is appropriate to deal with the

problem of estimating a highly dimensional parameter model, by allowing to divide the task

in smaller and simpler ones. In addition, given that Gibbs sampler is a stochastic algorithm,

it is more likely to escape local maxima.

Before summarizing the basic algorithm we need to clarify the choice of the factor

estimation method. If factors form a part of the unknown parameters of the TVP-FAVAR

model we need additional restrictions to identify it. Nonetheless, factors cannot be directly

identified since we cannot attribute a clear economic interpretation to them. On the other

hand, the main advantage of the static representation of the dynamic factor model, described

by equation 2.2, is that the factors can be estimated by the principal component method.

However, as discussed by Belviso and Milani (2006), the factors estimated by principal

component have unknown dynamic properties because principal components neither exploit

the factor nor the idiosyncratic component dynamics. There are two principal approaches

that exploit these features to extract the static factors through principal components. The

first is the tow-step approach situated in the frequency domain proposed by Forni et al.

(2005) and employed in the chapter 1. The second approach is a two-step strategy in the

parametric time domain introduced by Stock and Watson (2005). Therefore, we use Forni

98

et al. (2005)’s5 method to estimate the space spanned by the factors6. In order to choose

the appropriate number of estimated factors, we consider the sensitivity of the results to

the inclusion of a different number of factors. As explained in Bernanke et al. (2005), this

ad hoc way is justified by the fact that the statistical identification determines the number

of factors present in the data set but it does not determine the number of factors to use in

the model.

2.2.2.1 Prior distribution and starting values

In the choice of prior distribution of unknown parameters, we follow the specifications of

Primiceri (2005) and Koop and Korobilis (2009). Following the Bayesian literature, φ, Ht

and At will be called “parameters” and the covariance matrices of the innovations, i.e. the

elements of Q, and the break probabilities “hyperparameters”.

All the hyperparameters Qη except Qηψtare assumed to be distributed as independent

inverse-Wishart random matrices. The Wishart distribution can be thought of as the multi-

variate analog of χ-square, and used to impose positive definiteness of the blocks of Qη/−ψ.

Finally, the diagonal elements ψi of Q0ηψ

have univariate inverse Gamma distributions as

each ψi is a scalar.

Q0η ∼ IW (lη.(1+mη).VOLSη ,1+mη).

Q0ηψ∼ IG(lψ.(1+mψ).V

OLSψ ,1+mψ)

where VOLSψ denotes the variance of the OLS estimate of ψ and lψ are tuning constants. In

our case we do not use a training sample7 to estimate VOLSh as in Primiceri (2005), hence

VOLSh and VOLSη are assumed to be null matrices of dimension (mψ×mψ) and (mη×mη),

5For details of the dynamic factor model the reader is referred to Forni et al. (2005).6This method is, in addition, appropriate for samples with relatively small numbers of time ob-

servations. The choice of this method is therefore particularly appropriate since we use a quarterly

data sample with no more than 150 observations.7In this paper we do not use informative priors from training sample because our sample is already

relatively short and we are not prepared to sacrifice observations.

2.2. Methodology 99

respectively; m is the number of elements in the state vectors. IW (Sc ,df ) and IG(Sc ,df )

represent respectively the inverse-Wishart and the inverse-Gamma with scale matrix Sc and

degrees of freedom df . As in Primiceri (2005), lψ and lη are assumed to be equal to 0.07.

For all the parameters governing the structural break probabilities we assume that (π0) ∼

Beta(0.5,0.5), which indicates that there is a 50%8 chance of a break occurring in any time

period. Using uninformative priors we do not impose any constraint on the number of breaks

and we let the data speak for themselves.

The priors for the initial states of the regression coefficients, the covariances and

volatilities are assumed to be normally distributed, independent of each other and of the

hyperparameters. Let Θ0 = [Λ0 ψi ,0 φ0 a0 lnhi ,0]′ ∼ N(0,4I ), where I is the identity

matrix with dimensions of each respective parameter and 0 is a vector of 0’s. The choice

of zero mean reflects a prior belief that our variables will show little persistence since they

are used in first difference and are stationary. The variance scaling factor 4 is arbitrary but

large relative to the mean 0.

2.2.2.2 Simulation method

Conditional on using the conjugate priors and a Kalman filter, the Gibbs sampler is repeated

until convergence to the true posterior densities of the parameters. Note that at time t = 1

we do not need to choose an initial value of JΘ1 since whether we assume all parameters

are constant (JΘ1 = 0) or all are varying (JΘ1 = 1) does not affect the posterior results.

The states in JΘt are updated in the subsequent periods. Let a superscript T denote the

complete history of the data (e.g. ΘT = Θ′

1, ... ,Θ′

T ). We summarize the applied Gibbs

sampler involving the following steps:

1. Initialize the parameters (Θ0) and the estimated factors.

8E(π) = τ0τ1+τ1

.

100

2. Draw ΘT from p(ΘT |Y T ,Θ0) using Carter and Kohn (1994)’s algorithm, except for

h and ψ which are simulated using Kim et al. (1998)(1998)’s algorithm.

3. Draw hyperparameters QTηψ

using the inverse gamma distribution and the remaining

QTη hyperparameters are drawn from an inverse Wishart distribution.

4. Simulated the binary random variables JΘ using the Gerlach et al. (2000) algorithm.

5. Simulate πΘ(τ0,τ1), where τ0 = τ0+∑Tt=1 J

Θt and τ1 = τ1+T −∑

Tt=1 J

Θt .

6. Go to step 29

Conditional on initial values for the parameters (Θ0), except for ψi ,0 and lnhi ,0,

the estimated factors and the data Y T , the state-space form given by (2.1) and (2.2) is

linear and Gaussian. Therefore, the conditional posterior of ΘT is a product of Gaussian

densities and ΘT can be drawn using a forward-backward sampling algorithm from Carter

and Kohn (1994). Our objective is to characterize the marginal posterior densities of ΘT .

To obtain an empirical approximation to this density, the Gibbs sampler simulates ΘT from

the conditional density p(ΘT |Y T ,Θ0,FT ). This consists first, in updating the parameters

at time t conditional on data at time t (from t = 1 to T , each Θt is consecutively updated

conditional on data at time t). Then, the Kalman filter produces a trajectory of parameters

by again updating the estimated Θt using information in the subsequent periods (t +1).

Finally, from the terminal state ΘT , a backward recursion produces the required smoothed

draws by updating Θt conditional on information in previous periods from t = T −1 up to

t = 1, using the information from the whole sample.

However, drawing from the conditional posterior of ψi ,0 and lnhi ,0 is different because

the conditional state-space presentation for ψi ,0 and lnhi ,0 is non-normal. A Gibbs sampling

technique that extends the usual Gaussian Kalman filter, developed by Kim et al. (1998),

9Note that only factor loadings are considered as time-varying parameters. For this reason we

do not need to go back to step 1 in the algorithm. As explained above, factors are considered as

known parameters in the absence of theoretical justification of additional identification.

2.2. Methodology 101

consists of transforming the non-Gaussian state-space form into an approximately Gaussian

one, so that the Carter-Kohn standard simulation smoother can be employed.

In this second step, drawing parameters proceeds as follows. First, factor loadings

(ΛT ) are simulated conditional on prior distributions of estimated factors and data XT

(p(ΛT |XT ,FT )). Second, conditional on the sampled values of ΛT , a set of values of ψT

are drawn from the conditional distribution p(ψT |XT ,FT ,ΛT ). Third, coefficients (φT )

are simulated from the conditional density p(φT |Y T ,φ0,a0, lnh0). Fourth, the elements of

At are drawn from p(At |YT ,φT ,a0, lnh0). Finally, the diagonal elements of Ht are drawn

from p(At |YT ,φT ,aT , lnh0).

In step 3, conditional on Y T , estimated factor and ΘT , drawing from the conditional

posterior of the hyperparameters QTη/−ψ is standard, since it is a product of independent

inverse-Wishart distributions. However, since we have constrained the hyperparameter ma-

trix QTηψ

to be diagonal, its diagonal elements QTηψi

have univariate inverse-Gamma distribu-

tions. For the structural break probability parameters, the independent sequence of Bernoulli

variable JΘ is simulated non-conditional on data using Gerlach et al. (2000) algorithm10.

Finally, in step 5 the conditional posterior for the break probabilities π is sampled from Beta

distributions.

Given these marginal posterior densities, estimates of parameters and hyperparam-

eters can be obtained as the medians or means of these densities. The algorithm uses 60

000 sampling replications and discards the initial 40 000 as burn-in. When the posterior

moments vary little over retained draws, this means that the Gibbs sampler does converge

to the true posterior densities of the parameters.

10The algorithm proposed by Carter and Kohn (1994) draws J conditional on states Y T , but in

the presence of structural breaks or additive outliers J and Y T become highly correlated, making

this sampler very inefficient. The Gerlach et al. (2000) algorithm retains a high degree of efficiency

regardless of correlation between J and Y (Giordani and Kohn (2008)).

102

2.3 Empirical results

2.3.1 Data and preliminary results

In our application of the TVP-FAVAR methodology, the set of information variables is of

a balanced panel of 139 macroeconomic time series for Japan. The data are at quarterly

frequency and span the period from 1983:Q2 through 2008:Q4. The data set consists of

variables related to the real activity, consumer and producer price indexes, financial markets,

private and business anticipations and interest rates. As in Bernanke et al. (2005) our

data are classified into two categories of variables: we distinguish between “slow-moving”

variables which are predetermined in the current period and “fast moving” variables which

react contemporaneously to the economic news or shocks. The series have been demeaned

and standardized and seasonally adjusted when it is necessary and, as usual, the series are

initially transformed to induce stationarity. Our data set with the complete list of variables,

its sources and the relevant transformations applied, is presented in Table 1 in Appendix A.

As for the choice of monetary policy instrument for Japan, indicators vary from

study to study. As discussed in Inoue and Okimoto (2008), this choice is between the call

rate (Miyao (2000) and Nakajima et al. (2009a))11 and the monetary base (Shioji (2000)).

Inoue and Okimoto (2008) argue that the best choice is jointly considering the call rate and

the monetary base as policy indicators. This is because from 1995 onwards and particularly

from the introduction of QEMP in March 2001 to March 2006, interest rates were almost

zero and the monetary policy target was explicitly the monetary base. However, Inoue and

Okimoto (2008) finally consider only data spanning the period between January 1975 and

December 2002. This is because from October 2002 onwards the call rate was zero, in

which case the normality assumption is invalidated. Here, since our objective is to focus

on the QEMP period and for the reasons given in Inoue and Okimoto (2008) we assume

11Note that all of these studies use data from 1975 and 1977 to 1995 and 1998 and hence the

period of zero interest rate policy and QEMP are excluded.

2.3. Empirical results 103

that the monetary base is the only observable factor and then the only monetary policy

instrument.

In the first step, we need to determine the number of factors that characterize our

data set. Our results are not materially affected whether we choose three or four factors.

Bernanke et al. (2005) and Stock and Watson (2005) argue that three factors perform well

and since parsimonious modeling is always preferred, in our case we will also assume that

the data set can be described by three factors.

2.3.2 Specification tests

To carry out subsequent model selection, we opted for the Deviance Information Criterion

(DIC) statistic (Spiegelhalter et al. (2002)). The problem with the TVP-VARs is that it is

not easy to use the marginal likelihood, which is a typical measure for the Bayesian model, as

we have stochastic volatility which makes likelihood evaluations difficult and cumbersome.

The problem becomes more severe for the TVP-FAVAR model which has an additional

equation. The DIC takes into account two important features of the model: the complexity

(based on the number of the parameters) and the fit (typically measured by a deviance

statistic). DIC examines the two features together and gives a measure which balances

between the two. Table 2.1 shows the values of DIC estimated on 20,000 posterior means

draws for 5 different models with 3 factors and 2 lags: (i) a model with constant param-

eter (FAVAR), (ii) a model with only varying factor loadings (TVPL), (iii) a model with

varying factor loadings and auto-regressive terms (TVPLB), (iv) a model in which factor

loadings, auto-regression terms and covariance elements are assumed to vary (TVPLBA),

(v) a model where factor loadings, auto-regression terms and Log volatilities are assumed to

vary (TVPLBS) and (vi) a model in which all of the parameters are assumed to vary (TV-

PLBAS). Except FAVAR model all the other models are estimated for two kinds of priors:

uninformative priors (Beta(0.5,0.5)) and tightened priors (Beta(0.01,10)) for the transition

104

probabilities. With the latter priors we constrain the model to have few breaks (one or two

breaks) while with the uninformative priors the number of breakpoints is determined by the

data. Not surprisingly, the FAVAR model shows the highest DIC value, indicating that we

Table 2.1: Model comparison with Deviance Information Criterion (DIC)

FAVAR TVPL(2) TVPLB(2) TVPLBA(2) TVPLBS(2) TVPLBAS(2)

- 10421.3Few breaks - 10528.3 - 10530.0 -10531.7 -10610.9 -10651.3

a

uninformative -10529.1 -10530.4 10543.0 -10607.2 -10654.1

aResults are based on 60,000 iterations after a burn-in period of 40,000. The model

with smaller DIC would better predict a replicate datasets of the same structure.

need to take into account breaks in the model. All the other models perform clearly better,

corroborating the validity of a TVP approach. Then we test whether all parameters or few

of them vary over time. The resulting DIC of the unconstrained model (TVPLBAS-FAVAR)

is the lowest, hence all parameters do change over time. Next, we test whether the Japanese

economy is characterized by only a small number of breaks (e.g., among others, Fujiwara

(2006), Inoue and Okimoto (2008) and chapter 1 above). The comparison between models

with uninformative and informative priors tend to confirm the existence of more than two

breaks in the data (Nakajima et al. (2009a)). Even with informative priors results still indi-

cate a gradual evolution of the parameters. These outcomes tend to confirm our choice of

uninformative priors where the number of breakpoints is determined in a data based fashion.

2.3.3 The evolution of the Japanese monetary policy

Before examining the effectiveness of QEMP and its transmission channels, we need to

analyse the evolution of the Japanese monetary policy during the last three decades. In

Figure 2.1 we present the time-varying standard deviations of the errors in the equations

for the three factors, inflation, activity and the monetary base (i.e. the posterior means of

the square roots of the diagonal element of Ωt). Figure 2.1 shows that there is evidence of


time variation in error variances in all equations.

Figure 2.1: Posterior mean of the standard deviation of equation residuals

The figures show the time-varying standard deviations of the errors in the equations for the

three factors, inflation, activity and the monetary base.

The sharp increase in 1989 and in 1997 can be explained by the introduction of the

consumption tax (the consumption Tax Law took effect from 1 April 1989) and its increase

from 3 to 5 percent in April 1997. However, after early 1998 and until 2005 the volatility

is greatly reduced, reflecting the deflationary period experienced by the Japanese economy.

The volatility of GDP keeps increasing from the mid-1990s until 2001. This confirms the

106

findings of Nakajima et al. (2009a) that the variance of real GDP becomes higher in the

1990s than it was in the 1980s. One possible explanation is the increased uncertainty

that characterized the period after the burst of the asset price bubble and influenced the

investment. We particularly note the sharp decline in GDP volatility after the implementation

of the QEMP. In a similar way, we can think that during the QEMP period monetary

policy was more widely understood, and reducing the volatility of investment, reinforced the

perception that the business cycle had become less severe. Finally, the increase in monetary

base volatility from the end of 1995 corresponds to the decrease in the call rate to a lower

level in 1995 (0.5 %) and to nearly zero under the zero interest rate policy and QEMP.

2.3.4 Impulse response analysis

This section examines the dynamic relationships between variables through impulse response

functions which can be implemented for all series included in our database. We conduct

our analysis for three periods and dates are chosen in ad-hoc way: 1989 Q4, 1995 Q1 and

2002 Q1. The first date corresponds to the burst of the asset price bubble, the second

date represents the end of the use of the call rate as a monetary policy instrument and the

last date represents the beginning of the QEMP and the period when short-term interest

rate reached zero. The shock is normalized so that it increases the monetary base by its

standard deviation at all dates.

2.3.4.1 Was the QEMP effective?

Figure 2.2 displays impulse responses of key variables in the model to a monetary policy

shock over different dates chosen arbitrary: (i) 1989 Q1, before the burst of the asset

bubble and when interest rates were high, (ii) 1996 Q1, after the decline in the short term

interest rates to 0.5% and (iii) 2002 Q1, over the QEMP period. The posterior median is

the solid line and the broken lines are the 10th and 90th percentiles.


Figure 2.2: Impulse response functions

3 6 9 12 15 18 21-0.5

0

0.5

1

1.5M0, 89-Q1

3 6 9 12 15 18 21-0.1

0

0.1

0.2CPI Inflation, 2002-Q1

3 6 9 12 15 18 21-0.1

0

0.1


3 6 9 12 15 18 21

-0.1

0

0.1

0.2

Output, 89-Q1

3 6 9 12 15 18 21-0.5

0

0.5

1

1.5M0, 95-Q1

3 6 9 12 15 18 21-0.1

0

0.1


3 6 9 12 15 18 21

-0.1

0

0.1

0.2

Output, 2002-Q1

3 6 9 12 15 18 21

-0.1

0

0.1

0.2

Output, 95-Q1

3 6 9 12 15 18 21-0.5

0

0.5

1

1.5M0, 2002-Q1

The figures show the reactions of inflation and GDP to a shock to M0 over 21 quarters for three

different dates . The solid lines show the impulse responses implied by the time-varying FAVAR

(posterior median) and dashed lines represent the 10th and 90th percentiles.

It is not surprising that the effect of the monetary base shock on inflation and

GDP until 1995 is very weak and insignificant, indicating that monetary policy has been

considered as interest rate policy. However, from the second half of 1995 (second row) the

effect of the monetary base shock becomes positive but hardly significant. These results

are consistent with the evolution of the monetary base stochastic volatility from the end of

1995. During this period the interest rates fell to 0.5 percent and then declined further to

almost zero percent during the ZIRP period. It is then plausible to think that interest rates

108

being extremely low, the monetary base began to be used as an alternative policy instrument.

Interestingly, and in contrast with Fujiwara (2006) and Inoue and Okimoto (2008), during

the QEMP period (third row) inflation displays a positive and significant response, which

becomes statistically insignificant only after 3 quarters. This effect, though it is short-

lived, shows that the QEMP has an inflationary effect. The effect of the monetary base

shock on GDP is more pronounced. Production displays a temporary and not persistent

positive response, which veers to be insignificant after one year. This positive effect on

activity is unanimously detected in empirical studies. This temporary impact put together

with the decline in the output volatility leads us to think that monetary policy might be

the source of output fluctuations during the QEMP period. Note that the disconnection

between traditional VAR results and the standard theory predictions, that is revealed by

puzzles, price divergence and non-neutrality of money arising in Fujiwara (2006), Inoue and

Okimoto (2008) and Nakajima et al. (2009a), disappears under our rich-data model. As

shown in Bernanke et al. (2005) and Forni and Gambetti (2010), our results corroborate

the idea that a FAVAR methodology, which exploits a large set of information, improves the

accuracy of econometric models in predicting the effects of monetary policy, and, therefore,

could address puzzling effects observed otherwise.

In order to go further in our analysis we exploit the advantage of using TVP-FAVAR,

allowing us to observe the impulse responses to shocks for all the economic series included

in the construction of the factors. In doing so, we are able to detect the origin of the

QEMP effect. Figure 2.5 (in Appendix B) displays the reaction of disaggregated prices.

Except for two producer price indexes the reaction of the remaining prices is significantly

positive12. These results are in line with theory and are opposite to the so-called “price

puzzle” observed by Nakajima et al. (2009a) and Inoue and Okimoto (2008). An interesting

result that emerges from Figure 2.5 is that the monetary base shock has a positive effect

12We do not report the confidence intervals for lack of space.


on house prices (CPIHWEGFH), which are strongly correlated to the land price. According

to Kwon (1998), a large fraction13 of business investment financed by bank loans is secured

by land. It is therefore plausible to think that movements in land prices, whose values may

serve as collateral, can improve financing conditions and may play a significant propagating

role in the monetary transmission mechanism. As for disaggregated production, as shown in

Figure 2.6 (in Appendix B), except mining, a positive shock to the monetary base increases

all industrial production components, capacity utilization rates, shipments and to a lesser

extent earnings and employment. The employment rate remains fairly unaffected.

This result raises the question of the transmission mechanism through which the

QEMP affected the output and inflation. The QEMP can work through either policy-

duration channel or the portfolio-rebalancing channel, or both of them.

2.3.4.2 Policy-duration effect

The empirical validity of the policy-duration effect implied by theoretical studies is still an

open question. As shown by Eggertsson and Woodford (2003) and Jung et al. (2005), a

central bank can lower long-term interest rates by committing to the future zero interest

rates in advance, and so lower the real interest rates thanks to the inflation expectation.

Eggertsson and Woodford (2003) argue that this expectation channel is the only way to

escape deflation and stimulate an economy under a liquidity trap situation. Note that

lowering long-term interest rates is an intermediate objective and the ultimate objective

of monetary policy is price stabilization, which will hopefully facilitate economic growth.

Therefore, if this expectation channel is effective the economic recovery should increase

expected inflation and thus future short-term interest rates, which, in turn, will raise long-

term interest rates. From Figure 2.3, we see that during the period of QEMP the reaction

13Of total secured bank loans, about 45% have been collateralized by land, while only about 3%

have been backed up by stocks and bonds. Thus, land prices might be closely related to real activities

in Japan.

110

of private-sector (HHE) and business-sector (DIBSE) expectations is significant but short-

lived.

Figure 2.3: Impulse responses - Policy-duration effect

3 6 9 12 15 18 21

-0.2

-0.1

0

0.1

0.2

JGB 10Y, 89-Q1

3 6 9 12 15 18 21

-0.4

-0.2

0

0.2

JGB 10Y, 95-Q1

3 6 9 12 15 18 21

-0.2

-0.1

0

0.1

0.2

JGB 10Y, 2002-Q1

3 6 9 12 15 18 21-0.5

0

0.5LT 5Y, 89-Q1

3 6 9 12 15 18 21-0.4

-0.2

0

0.2

0.4

LT 5Y, 95-Q1

3 6 9 12 15 18 21-0.2

0

0.2

0.4

LT 5Y, 2002-Q1

3 6 9 12 15 18 21

-0.4

-0.2

0

0.2

DIBSE, 89-Q1

3 6 9 12 15 18 21

-0.4

-0.2

0

0.2

DIBSE, 95-Q1

3 6 9 12 15 18 21

-0.4

-0.2

0

0.2

DIBSE, 2002-Q1

3 6 9 12 15 18 21

-0.4

-0.2

0

0.2

0.4HHE, 89-Q1

3 6 9 12 15 18 21

-0.4

-0.2

0

0.2

0.4

HHE, 95-Q1

3 6 9 12 15 18 21

-0.4

-0.2

0

0.2

0.4

HHE, 2002Q1

The figures show the reactions of five-year JGBs’s yields (LT 5Y), long-term JGBs’s yields (JGB

10 Y), private sector (HHE) and business-sector (DIBSE) expectations to a shock to M0 over

21 quarters for three different dates. The solid lines show the impulse responses implied by the

time-varying FAVAR (posterior median) and dashed lines represent the 10th and 90th percentiles.

In contrast with Nakajima et al. (2009a) and Kimura et al. (2003), the impulse

responses of medium- (LT 5Y) and long-term (JGB 10 Y) interest rates are insignificant.

However, these results need to be interpreted carefully and should not be taken as evidence

against the expectation channel (neo-Wicksellian view). The positive effect on private and

business sector expectations, even short-lived, can also be interpreted as a successful BOJ

policy commitment in preventing a downward spiral of expectations. However, as argued

in Nakajima et al. (2009a), the policy commitment, alone, is not sufficient to generate

significant inflationary pressure to escape from the trap of deflationary phase and to lead to


upward shifts in the trend growth path. In order to better analyze the policy-duration effect,

a more appropriate model examining the interactions between the macroeconomic variables

and the yield curve is needed. This will be the subject of the next chapter.

2.3.4.3 Portfolio-rebalancing channel

The portfolio-rebalancing channel is supposed to be induced indirectly by the increase in the

CAB or directly by the increase in BOJ’s JGB purchases. As prices rise for JGBs their yields

will fall relative to those of other assets. Households and companies may be encouraged

to switch into other type of assets in search of higher returns. That would push up other

asset prices as well. Similarly, households and companies use the additional money injected

by the central bank to buy alternative non-monetary assets, increasing their prices. The

stock price (TOPIX), which serves as a proxy for financial asset prices, increases in reaction

to monetary base expansion but becomes insignificant only after around 6 quarters (Figure

2.4). As investors’ demand for alternative assets such as equities increases, the ability

of businesses to raise finance in capital markets improves and the cost falls. By contrast

with Oda and Ueda (2007) and Kimura et al. (2003), these results show that the portfolio-

rebalancing channel could have a role in transmitting monetary policy shocks. It is likely that

the QMEP was effective through the stock price channel. As explained in chapter 1, there

are four possible channels through which higher stock prices boost output: an increase

in consumption through a rise in households’ wealth (the wealth effect); an increase in

investment through higher Tobin’s q; an increase in bank lending through a decline in the

external finance premium of borrowers (the balance sheet effect); and an increase in bank

lending through an improvement in the banks’ capital-to-asset ratios.

112

Figure 2.4: Impulse responses - Portfolio-rebalancing channel

3 6 9 12 15 18 21-0.5

0

0.5

Consumption, 89-Q1

3 6 9 12 15 18 21-0.5

0

0.5

Consumption, 95-Q1

3 6 9 12 15 18 21-0.5

0

0.5

Consumption, 2002-Q1

3 6 9 12 15 18 21-0.05

0

0.05Bank lending, 89-Q1

3 6 9 12 15 18 21-0.05

0


3 6 9 12 15 18 21-0.05

0


3 6 9 12 15 18 21-0.2

0

0.2

0.4

TOPIX, 89-Q1

3 6 9 12 15 18 21-0.2

0

0.2

0.4

TOPIX, 95-Q1

3 6 9 12 15 18 21-0.2

0

0.2

0.4

TOPIX, 2002-Q1

The figures show the reactions of the consumption, the bank lending and asset prices (TOPIX) to

a shock to M0 over 21 quarters for three different dates. Solid lines show the impulse responses

implied by the time-varying FAVAR (posterior median) and dashed lines represent the 10th and

90th percentiles.

While bank lending does not react significantly to the monetary base shock, con-

sumption14 increases significantly during the QEMP period but this reaction is short-lived.

Therefore, we suppose that the stock price channel is driven mainly by the wealth effect

and investment15. The increase in the stock price may have helped Japanese firms restore

their balance sheets, which were destroyed after the asset price bubble burst and land prices

collapsed in the early 1990s16. Companies therefore started investing their profits instead

of using them to repay debts.

Our findings suggest that QEMP is effective and works through both monetary policy

14This correponds to the total consumption for 2 or more persons (variable number 49 in the list

of variables in Appendice A.)15The data for private investments are available only from 199416As argued in Koo (2008), the corporate sector was busy repaying debt until 2004; net debt

repayments fell to zero by the end of 2005.


commitment and portfolio-rebalancing channel. This is in line with Bernanke and Reinhart

(2004)’s suggestions that the neo-Wicksellian policy commitment needs to be complemented

with more aggressive use of monetarist approaches to monetary policy. The authors also

argue that the BOJ should not have to limit changes to the composition of its balance

sheet to only focus mainly on purchases of government securities but that it should extend

its open market purshases to a wide range of securities. The recommendations addressed

by Bernanke and Reinhart (2004) to the BOJ were put into practice by Ben Bernanke, as

chairman of the Federal Reserve System, in order to combat the current financial crisis.

The non-conventional monetary policy strategy adopted by the Fed called credit easing, is

similar to QEMP in its explicit commitment to maintaining the nominal short-term interest

rate at low levels. However, the main difference between the two strategies is that the Fed,

through its Credit Easing, focuses on the change in the composition of its balance sheet by

purchasing a wide range of securities17, yet the size of the balance sheet remains a secondary

objective. Moreover, Gagnon et al. (2010) show that credit easing mainly worked through

the portfolio-rebalancing channel, the decline in long-term interest rates being attributed

to the decline in term premia and not to the expectation of low future short-term interest

rates. The authors argue that the large-scale asset purshases (LSAPs) implemented by the

Fed not only reduced longer-term yields on the assets being purshased (agency MBS and

Treasury securities), but also reduced yields on other assets (corporate bonds and equities).

This complementarity between the portfolio-rebalacing channel and the expectation

channel is, moreover, corroborated by the fact that the BOJ, building on its past experience

with QEMP, recently implemented “Comprehensive Monetary Easing” (CME). This strategy

17The Fed’s experience of credit easing comprises two courses of action. First, there is an ex-

plicit commitment to maintaining the nominal short-term interest rate at low levels. Second, the

Fed implements large-scale asset purchases (LSAPs), which range from housing agency debt and

mortgage-backed securities (MBS) to long-term Treasury securities. However, the Bank of England

and the ECB associated their operating procedure on a monetarist view of the transmission process.

They began a programme of large-scale asset purchases in 2009 without any explicit commitment

to maintaining their policy rates at low levels.

114

focuses more on changes in balance sheet composition and on the extension of open market

purchases to a wide range of securities18.

2.4 Conclusion

Recent research has employed VAR models, accounting for regime changes, leading

to advances in the measurement of the effect of Japanese quantitative easing. These models

permit researchers to verify whether or not the Japanese monetary policy has undergone

structural changes. This issue is particularly important for the Japanese economy in the last

two decades. The main shortcoming of this literature has been the inability to incorporate

larger and more realistic information sets related to central banks and the private sector.

This chapter employed a time-varying parameters FAVAR (TVP-FAVAR) model to overcome

these limitations. This model allowed us both to take into account regime changes and to

measure the effects of monetary policy shocks on numerous variables.

Our analysis delivers four main results. First, unsurprisingly, our results suggest

that the best model to specify Japanese monetary policy during the two last decades is a

model where all parameters vary over time. This corroborates our choice of a time varying

parameters model. Second, the effect of QEMP on activity and prices is stronger than

previously found. In particular, we find a significant price reaction to a monetary policy

shock. Moreover, the problem related to the price puzzle, the price divergence and the

non-neutrality of money that arises in previous works disappears under our data-rich model.

18In October 2010, the BOJ announced the adoption of the new monetary strategy called “Com-

prehensive Monetary Easing” in reference to its past experience of QEMP. This strategy approaches

credit easing as implemented by the Fed, consisting of the following two principal courses of action.

First, as in QEMP, the BOJ commits to maintaining short-term interest rates at around 0 to 0.1

percent. Second, the BOJ increases the amount of outright purchases not only of government securi-

ties, but also of commercial paper, corporate bonds, exchange-traded funds and Japanese real estate

investment trusts. Note that in contrast to QEMP, CME puts the emphasis on the composition of

the BOJ’s balance sheet without any explicit reserve level target.

2.4. Conclusion 115

Third, by contrast with previous work, there is a detectable effectiveness of the portfolio-

rebalancing channel, which could have a role in transmitting monetary policy shocks. The

weak reaction of bank lending and the significant increase in consumption, even short-lived,

lead to think that the positive and significant asset price reaction generates two main effects:

it means lower yields, reducing the cost of borrowing for households and companies, leading

to higher consumption and investment spending. It also means that the wealth of the asset

holders increases, which should boost their spending. Fourth, while the policy commitment

succeeds in controlling private and business expectations, the reaction of medium to long-

end of the yield curve remains insignificant.

Moreover, one interesting result that emerges from the price reaction is that the

monetary base shock has a positive effect on house prices, which are strongly correlated to

the land price. A large fraction of business investment financed by bank loans is secured

by land. It is therefore plausible to think that movements in land prices, whose values may

serve as collateral, can improve financing conditions and may play a significant propagating

role in the monetary transmission mechanism.

These results shoud not be taken as evidence in favor of portfolio-rebalancing channel

against the expectation channel. The positive but short-lived effect on private and business

sector expectations may not be sufficient to restore the previous trends in prices and out-

put, but might prevent downward spiral of expectations. Therefore, the two channels are

complementary rather than exclusive. On the other hand, since the expectations hypothesis

of the term structure of interest rates is a necessary condition for the effectiveness of the

expectation channel, we think that a macro-finance model is more appropriate to better

analyse the effectiveness of the policy-duration effect. This will be the issue of the next

chapter.

116

Appendix A : Data and transformations

Table 2.2 – Variable listData are extracted from Reuters EcoWin database. The transformation codes (T)

are : 1 – no transformation ; 2 – first difference ; 4 – logarithm ; 5 – first difference of

logarithm. In this database VRAI means seasonally adjusted.# Mnemonic T Description

Slow moving1 IPT 5 Industrial Production Total Index

2 IPSCP 5 Production, Ceramics, stone and clay products, Index

3 IPCH 5 Production, Chemicals, Index

4 IPVEH 5 Production, Industrial vehicle, Index

5 IPDVEH 5 Production, Domestic vehicle, total

6 IPFM 5 Production, Fabricated metals, Index

7 IPFT 5 Production, Food and tobacco, Index

8 IPGM 5 Production, General machinery, SA, Index

9 IPIS 5 Production, Iron and steel, Index

10 IPMANUF 5 Production, Manufacturing, Index

11 IPMMANUF 5 Production, Mining and manufacturing, Index

12 IPNFM 5 Production, Non-ferrous metals, Index

13 IPOMUNUF 5 Production, Other manufacturing, Index

14 IPPCP 5 Production, Petroleum and coal products, Index

15 IPPP 5 Production, Plastic products, Index

16 IPPI 5 Production, Precision instruments, Index

17 IPIP 5 Production, By industry, paper, Index

18 IPCE 5 Production, Communication Equipment, Index

19 IPSD 5 Production, Semiconductor devices, Index

20 IPTEXT 5 Production, Textiles, Index

21 IPTRANSPE 5 Production, Transport equipment, Index

22 SHIPMCGEXTE 5 Shipments, Capital goods excl transport equipment„ Index

23 SHIPMAG 5 Shipments, Capital goods, SA, Index

24 SHIPMCE 5 Shipments, Communication Equipment , Index

25 SHIPMCONSTG 5 Shipments, Construction goods,Index

26 SHIPMCONSUMG 5 Shipments, Consumer goods, Index

27 SHIPMDCG 5 Shipments, Durable consumer goods, Index

28 SHIPMING 5 Shipments, Investment goods , Index

29 SHIPMMANUF 5 Shipments, manufacturing, Index

30 SHIPMMMANUF 5 Shipments, Mining and manufacturing, Index

31 SHIPMNDCG 5 Shipments, Non-durable consumer goods, Index

32 SHIPMPG 5 Shipments, Producer goods total, Index

33 SHIPMPGMMANUF 5 Shipments, Producer goods, for mining and manufacturing, Ind

34 SHIPMPGOTHERS 5 Shipments, Producer goods, for others„ Index

35 CAPUORCH 5 Capacity Utilization, Operation Ratio,Chimicals

36 CAPUORFM 5 Capacity Utilization, Operation Ratio, Fabricated metals

37 CAPUORGM 5 Capacity Utilization, Operation Ratio, General machinery

38 CAPUORIS 5 Capacity Utilization, Operation Ratio, Iron and steel

39 CAPUORMINDUS 5 Capacity Utilization, Operation Ratio, Machinery industry

40 CAPUORMNUF 5 Capacity Utilization, Operation Ratio, Manufacturing

41 CAPUORPC 5 Capacity Utilization, Operation Ratio, Petroleum and coal

42 CAPUORPPP 5 Capacity Utilization, Operation Ratio, Pulp, paper and pap

43 CAPUORTEXT 5 Capacity Utilization, Operation Ratio, Textiles

44 CAPUORTE 5 Capacity Utilization, Operation Ratio, Transport equipment

45 HWAVGC 5 Hours Worked, Average Per Month, Construction

46 HWAVGMANUF 5 Hours Worked, Average Per Month, Manufacturing

47 HWAVGMIN 5 Hours Worked, Average Per Month, Mining

2.4. Conclusion 117

48 CONSGENEXCLHA 5 Japan, Index of Consumption Expenditure Level, 2 or more persons, ge-

neral excl housing, automobiles, money gifts & remittance, Vrai, Index,

JPY, 2000=100

49 CONSGENERAL 5 Japan, Consumer Surveys, Index of Consumption Expenditure Level, 2

or more persons, general, Vrai, Index, JPY, 2000=100

50 CONSHOUSING 5 Japan, Consumer Surveys, Index of Consumption Expenditure Level, 2

or more persons, housing, Vrai, Index, JPY, 2000=100

51 CONSTRANSCOM 5 Japan, Consumer Surveys, Index of Consumption Expenditure Level, 2

or more persons, transportation & communication, Vrai, Index, JPY,

2000=100

52 UNEMP 5 Unemployment, Rate, SA

53 EMPTRATE 5 Employment, Overall, Total

54 EMPCONST 5 Employment, By Industry, Construction, Index

55 EMPGOV 5 Employment, By Industry, Government

56 EMPMANUF 5 Employment, By Industry, Manufacturing

57 EMPALLINDUST 5 Employment, By Status, Regular employees, all industries

58 JALFT 5 Japan, Activity, Labour Force, Total

59 SDST 5 Sales at Deapartement Stores, Total, Index

60 CPIALL 5 Japan, Consumer Prices, Industrial products,All, Index, JPY, 2000=100

61 CPIINDP 5 Japan, Consumer Prices, Industrial products,Textile, Index, JPY,

2000=100

62 CPIINDT 5 Japan, Consumer Prices, Electricity, gas & water charges , Index, JPY,

2000=100

63 CPIEGW 5 Japan, Consumer Prices, Services , Index, JPY, 2000=100

64 CPISERV 5 Japan, Consumer Prices, Durable goods , Index, JPY, 2000=100

65 CPIDG 5 Japan, Consumer Prices, Non Durable goods , Index, JPY, 2000=100

66 CPINDG 5 Japan, Consumer Prices, Food , Index, JPY, 2000=100

67 CPIFOOD 5 Japan, Consumer Prices, Reading and Recreation , Index, JPY,

2000=100

68 CPIRR 5 Japan, Consumer Prices, Nationwide, Miscellaneous Goods and Ser-

vices, Durable goods, Index, JPY, 2000=100

69 CPIGSDG 5 Japan, Consumer Prices, Nationwide, Transport, Private transporta-

tion, Index, JPY, 2000=100

70 CPITPT 5 Japan, Consumer Prices, Nationwide, Transport, Public transportation,

Index, JPY, 2000=100

71 CPITPUBT 5 Japan, Consumer Prices, Nationwide, Communication, Communica-

tion, Index, JPY, 2000=100

72 CPICC 5 Japan, Consumer Prices, Nationwide, Housing, Water, Electricity, Gas

and Other Fuels, Electricity, Index, JPY, 2000=100

73 CPIWEG 5 Japan, Consumer Prices, Nationwide, Health, Medical treatment, In-

dex, JPY, 2000=100

74 CPIHMT 5 Japan, Consumer Prices, Nationwide, Health, Medical care, Index, JPY,

2000=100

75 CPIHMC 5 Japan, Corporate Goods Prices, Domestic demand products, consumer

goods, Index, JPY, 2000=100

76 PPIDDPCG 5 Japan, Corporate Goods Prices, Domestic demand products, final

goods, Index, JPY, 2000=100

77 PPIDDPFG 5 Japan, Corporate Goods Prices, Domestic demand products, nondu-

rable consumer goods, Index, JPY, 2000=100

78 PPIDDPNCG 5 Japan, Corporate Goods Prices, Domestic demand products, total, In-

dex, JPY, 2000=100

79 PPIDDPT 5 Japan, Corporate Goods Prices, Domestic, capital goods, Index, JPY,

2000=100

80 PPIDCG 5 Japan, Corporate Goods Prices, Domestic, chemicals, Index, JPY,

2000=100

81 PPIDCH 5 Japan, Corporate Goods Prices, Domestic, consumer goods, Index,

JPY, 2000=100

82 PPIDT 5 Japan, Corporate Goods Prices, Domestic, total, Index, JPY, 2000=100

83 PPISERVALL 5 Japan, Corporate Service Prices, All items, Index, JPY, 2000=100

118

84 PPISERVT 5 Japan, Corporate Service Prices, Transportation, Index, JPY,

2000=100

85 PPIFINS 5 Japan, Corporate Service Prices, Finance and insurance, Index, JPY,

2000=100

86 EXPORT 5 Japan, Exports, Volume, Total, Index, JPY, 2000=100

87 IMPORT 5 Japan, Imports, Volume, Total, Index, JPY, 2000=100

Fast moving88 CONSTSTARTEDP 4 Japan, construction started, Private

89 CONSTSTARTEDPUB 4 Japan, construction started, Public

90 CONSTSTARTEDT 4 Japan, construction started, Total

91 HSBS 4 Housing Starts, Housing built for sale

92 HSRH 4 Housing Starts, Rental homes

93 HST 4 Housing Starts, Total

94 NEWORDCONSP 5 Japan, New Orders, Construction, Private sector, JPY

95 NEWORDCONST 5 New Orders, Construction, Total, Big 50 constructors, JPY

96 NEWORDIM 5 Japan, New Orders, Machine Tools, By industry, machine and equip-

ment industries, industrial machinery, JPY

97 NEWORDMTT 5 Japan, New Orders, Machine Tools, By industry, machine and equip-

ment industries, total, JPY

98 NEWORDCMANUF 5 Japan, New Orders, Construction, Manufacturing, JPY

99 JDFFTSET 5 Japan, Daiwa, Free float, TSE, Total Index, JPY

100 JDFFTSETU 5 Japan, Daiwa, Free float, TSE, Transportation & Utilities Index, JPY

101 TOPIX 5 Japan, Tokyo SE, Topix Index, Price Return, End of Period, JPY

102 DOLLARYEN 5 US.Dollar/Yen Spot Rate, Average in the Month, Tokyo Market

103 EFFEXCHANGE 5 Japan, BIS, Nominal Narrow Effective Exchange Rate Index, Average,

JPY

104 M1 5 Japan, M1, outstanding at end of period, Vrai, JPY

105 M2CDs 5 M2+CDs/Average Amounts Outstanding/(Reference) Money Stock

106 M3 5 Japan, M3, outstanding at end of period, JPY

107 BOJAAL 5 Japan, BOJ accounts, assets, loans, JPY

108 BOJAAT 5 Japan, BOJ accounts, assets, total, JPY

109 DLBABD 5 Japan, Domestically Licensed Banks, Assets, bills discounted, JPY

110 DLBACL 5 Japan, Domestically Licensed Banks, Assets, call loans, JPY

111 DLBACLBD 5 Japan, Domestically Licensed Banks, Assets, loans and bills discounted,

JPY

112 DLBCBALBD 5 Japan, Domestically Licensed Banks, City banks, assets, loans and bills

discounted, JPY

113 DLBRBALBD 5 Japan, Domestically Licensed Banks, Regional banks, assets, loans and

bills discounted, JPY

114 DLBAL 5 Japan, Domestically Licensed Banks, Assets, loans, JPY

115 DLBCBAL 5 Japan, Domestically Licensed Banks, City banks, assets, loans, JPY

116 DLBRBAL 5 Japan, Domestically Licensed Banks, Regional banks, assets, loans,

JPY

117 INVINVG 5 Inventory Investment goods, Index

118 INVMMANUF 5 Inventory Mining and manufacturing, Index

119 INVFM 5 Inventory Fabricated metals, Index

120 INVCG 5 Inventory Construction goods, Index

121 INVCAPG 5 Inventory Capital goods, Index

122 INVNDCG 5 Inventory Non-durable consumer goods, Index

123 INVCONSUMG 5 Inventory Consumer goods, SA, Index

124 INVPG 5 Inventory Producer goods, Index

125 PLRLT 1 Japan, Prime Rates, Prime Lending Rate, Long Term, End of Period,

JPY

126 PLRST 1 Japan, Prime Rates, Prime Lending Rate, Short Term, End of Period,

JPY

127 TB3M 1 Japan, Treasury Bills, Bid, 3 Month, Yield, End of Period, JPY

128 TIOR3M 1 Tokyo interbank offered rates (3 months)

129 JGB10 1 Yield of Government Bonds (10 Y)

2.4. Conclusion 119

130 SP10TIOR3M 1 Spread rate : Yield of Government Bonds (10 Y) - Tokyo Interbank

Offered Rate (3 M)

131 IBGB10 1 10-year interest-bearing Government Bonds

132 LGB10 1 10-year Local Government Bonds

133 GGB10 1 10-year Government Guaranteed Bonds

134 IBBD5 1 5-year interest-bearing Bank debentures

135 CALLRATE 1 Japan, Interbank Rates, Uncollateralized, O/N, Average, JPY

136 SPIBBD5TIOR3M 1 Spread between the Yield on long-term and short-term : Yield of Go-

vernment Bonds (5 Years) - Tokyo Interbank Offred Rate (3 months)

137 SPGGB10TIOR3M 1 Spread between the Yield on long-term and short-term : Yield of Go-

vernment Guaranteed Bonds (10 Years) - Tokyo Interbank Offred Rate

(3 months)

138 DIBSE 1 DI/Business Conditions/All industries/Forecast

139 HHE 1 Consumer Surveys, Consumer Confidence, Including one-person house-

holds, total

120

Appendix B : Impulse response functions for price and activityvariables

Figure 2.5 – Impulse responses - Disaggregated price

3 6 9 12 15 18 21-0.5

-0.25

0

0.25

0.5

0.75

1M0

89-Q1

95-Q2

01-Q1

3 6 9 12 15 18 210

0.25

IRF of CPITTC

3 6 9 12 15 18 21

0

0.25

IRF of CPICC

3 6 9 12 15 18 210

0.25

IRF of CPIHWEGFH

3 6 9 12 15 18 210

0.25

IRF of CPIMGSM

3 6 9 12 15 18 210

0.25

IRF of PPIDDPCG

3 6 9 12 15 18 21

0

IRF of PPPDDPDCG

3 6 9 12 15 18 210

0.25

IRF of PPIDDPNDCG

3 6 9 12 15 18 21

0

0.25

IRF of PPIDDPPGT

3 6 9 12 15 18 210

0.25

0.5

IRF of PPITEXT

3 6 9 12 15 18 21

0

0.25

IRF of PPIDT

3 6 9 12 15 18 21

-0.25

0

IRF of PPIDTE

The figures show the reactions of some selected prices to a shock to M0 over 21 quarters for

three different dates . The solid lines show the impulse responses implied by the time-varying

FAVAR (posterior median) and dashed lines represent the 10th and 90th percentiles. Details on

nomenclatures are given in Appendice A.

2.4. Conclusion 121

Figure 2.6 – Impulse responses - Disaggregated production

3 6 9 12 15 18 21-0.5

-0.25

0

0.25

0.5

0.75

1M0

89-Q1

95-Q2

01-Q1

3 6 9 12 15 18 210

0.25

IRF of IPCE

3 6 9 12 15 18 21-0.5

-0.25

0

0.25

0.5

0.75

1

IRF of IPGM

3 6 9 12 15 18 21-0.5

-0.25

0

0.25

0.5

0.75

1

IRF of IPV

3 6 9 12 15 18 21-1

-0.75-0.5

-0.250

0.250.5

0.751

IRF of IPMANUF

3 6 9 12 15 18 21

0

IRF of IPMIN

3 6 9 12 15 18 21-0.5

-0.25

0

0.25

0.5

IRF of IPTRANSE

3 6 9 12 15 18 21-0.5

-0.25

0

0.25

0.5

0.75

1

IRF of SHCG

3 6 9 12 15 18 21-0.5

-0.25

0

0.25

0.5

0.75

1

IRF of SHCONSUMG

3 6 9 12 15 18 210

0.25

0.5

0.75

1

IRF of SHDCG

3 6 9 12 15 18 21-0.5

-0.25

0

0.25

0.5

0.75

1

IRF of SHINVG

3 6 9 12 15 18 21-1

-0.75-0.5

-0.250

0.250.5

0.751

IRF of SHMMANUF

3 6 9 12 15 18 210

0.25

0.5

0.75

1

IRF of CAPMI

3 6 9 12 15 18 210

0.25

0.5

0.75

1

IRF of CAPMANUF

3 6 9 12 15 18 21-0.5

-0.25

0

0.25

0.5

IRF of CAPTRANSE

3 6 9 12 15 18 21

0

IRF of EAVGMALL

3 6 9 12 15 18 21

0

IRF of EMPT

3 6 9 12 15 18 21

0

IRF of EMPGVT

3 6 9 12 15 18 210

0.25

0.5

0.75

1

IRF of NEWJOB

3 6 9 12 15 18 21

0

IRF of UNEMPR

The figures show the reactions of some selected variables related to activity to a shock to M0

over 21 quarters for three different dates . The solid lines show the impulse responses implied by

the time-varying FAVAR (posterior median) and dashed lines represent the 10th and 90th percen-

tiles.Details on nomenclatures are given in Appendice A.

122

33Quantitative Easing and the Time-Varying

Dynamics of the Term Structure of Interest rate

in Japan

3.1 Introduction

In a zero interest rate environment the short-term interest rate is no longer a policy instru-

ment under the direct control of the central bank. The alternative monetary policy used by

most major central banks is monetary easing. The goal of the central bank is therefore to

impact the economy across the yield curve, bringing down long-term interest rates, thereby

boosting the economy. The aim of this chapter is to examine the effectiveness of such a

policy in affecting the yield curve using the Japanese experience of QEMP. We are particu-

larly interested in analyzing the possible bi-directional feedback from the yield curve to the

economy.

123

124

The QEMP as implemented by the BOJ comprised three courses of action namely,

(i) injecting ample liquidity into the market using the current account balances (CAB) as the

main monetary policy instrument, (ii) making a commitment to maintain short-term rates at

around zero until the CPI inflation stabilized at zero percent or increased year after year and

(iii) purchasing more of long-term Japanese government bonds (JGBs). The transmission

mechanisms suggested by this policy are the portfolio-rebalancing channel (Metzler (1995))

and the expectation channel which consists of the policy-duration effect (Krugman (2000)

and Eggertsson and Woodford (2003)) and the signaling effect.

This chapter evaluates the effectiveness of the QEMP, focusing on the expectation

channel. The effectiveness of such a channel depends totally on the credibility of the central

bank’s policy of maintaining the future short-term interest rate at a near zero level. The

desired intermediate effect of the monetary policy is that the reduction of expected future

short-term rates will be transmitted to the long end of the yield curve. The decline in the

long-term interest rates will, in turn, lead to increased expectations of inflation and stimulate

activity.

Several papers have examined the effectiveness of this transmission channel by fo-

cusing on the term structure of interest rates. Oda and Ueda (2007), Okina and Shiratsuka

(2004a) and Baba et al. (2005) show that policy duration has a clear and significant effect,

lowering the yield curve. The common point of these papers is that they do not examine

the transmission of this positive effect to the real economy. On the other hand, Evans and

Marshall (2007) and Ang and Piazzesi (2003) examine the joint dynamics of bond yields and

macroeconomic variables in a vector autoregression. Ang and Piazzesi (2003) show that a

substantial portion of short- and medium-term bond yields is explained by macroeconomic

variables. In contrast, Evans and Marshall (2007) find that macroeconomic variables do also

explain much of the long-term bond yield dynamics. For the Japanese case, Nakajima et al.

(2010), using a time-varying-parameter VAR model (TVP-VAR), show some evidence of the


effectiveness of policy duration in lowering five-year JGB yields, although this effect is not

transmitted to the real economy. One potential drawback of such models is that few specific

yields are taken into consideration. The results may therefore not reflect to full range of term

structure. Diebold et al. (2006) examine the interactions between the macroeconomy and

the yield curve by means of the one-step Kalman filter approach. They aggregate information

from a large set of yields using latent factors, which represent the level, the slope and the

curvature, based on Nelson and Siegel (1987)’s model. Using this model, which allows for

correlated latent yield factors and observed macroeconomic variables, Diebold et al. (2006)

show that macroeconomic variables have strong effects on future movements of the yield

curve, while latent interest rate factors have a relatively small impact on macroeconomic

variables.

Needless to say, it could be unrealistic to assume time-invariance either in monetary

policy transmission mechanisms or in the structure of the Japanese economy. Indeed, as

already documented in several papers for the Japanese economy (Miyao (2000), Fujiwara

(2006), Inoue and Okimoto (2008), Nakajima et al. (2009a) and chapter 1), there is clear

evidence of significant structural changes in the last two decades. As shown in Cargill et al.

(2001), the Japanese economy has become very unstable having experienced significant

institutional and monetary strategy changes during the “lost” decade. For these reasons and

for the first time to the best of our knowledge, we employ a generalized Nelson-Siegel model

with time-varying coefficient and stochastic volatilities, as described in Bianchi et al. (2009),

for the Japanese case. This study differs from previous studies on the JGB market on three

points. Firstly, it focuses on a more complete set of possible macroeconomic variables and

monetary policy instruments and their possible effects on the term structure of interest

rates. Secondly, it allows us to take into account potential instabilities both in monetary

policy transmission mechanisms and in the relationship between yield curve and the structure

of the economy. Finally, the data sample in this study is significantly larger ; data span the

126

period between February 1985 and October 2009. This allows us to analyze the relationship

between yield curve and macroeconomic and monetary variables under different economic

conditions, i.e. periods with different monetary policy strategies.

The objective of this work is twofold. First, we propose a constrained smoothing

B-splines method to estimate the term structure of spot rates using JGB prices. Given

that short term interest rates were even lower than usual during the QEMP period, using

traditional yield curve models could result in negative values for yields with short maturity

during this period. In order to overcome this problem we incorporate non-negative restrictions

in the smoothing B-splines method. Second, we apply a time-varying parameter macro-

finance model to data on JGBs with 17 different maturities as well as three macroeconomic

variables, namely, output gap, inflation and monetary policy instrument.

Contrary to the results of standard term-structure models with time-invariant term

premia, our results show that the expectations hypothesis of the term structure of interest

rates is generally supported even during the QEMP period. This is a necessary condition for

the effectiveness of the expectation channel. Moreover, the estimation results reveal that

the contribution of macroeconomic variables to the yield curve variation is relatively small,

especially during the QEMP period. As for the feed-back effect, the yield curve factors

contribute only marginally to inflation variation. They account for a greater part of output

gap dynamics during the period of high interest rates, but these effects revert to a much

lower level during the QEMP period. These results indicate that during the deflationary

period and under the zero lower bound of interest rates the relationship between financial

markets and macroeconomic variables becomes very weak. These findings are corroborated

by the impulse response functions. The monetary policy shock has a significant effect on the

yield curve level factor only during the high interest rate periods. However, during the QEMP

period the decline in the level factor, while insignificant, indicates strengthening credibility

of the BOJ and thus the effectiveness of its policy.

3.2. Estimating spot rate curves for Japan 127

The rest of the chapter is structured as follows. The next section presents the

term structure estimation and the data construction. Section 3 describes the time-varying

parameter macro-yield model. The results are presented in section 4 and finally section 5

concludes.

3.2 Estimating spot rate curves for Japan

3.2.1 Data construction

We base our analysis on data originating from two different sources and covering the

period from 1985 :02 to 2009 :10. Data were provided by Tokyo Stock Exchange (TSE)

and Japan Securities Dealers Association (JSDA)1. The data set comprises beginning-of-

month observations of the officially quoted prices, remaining maturities and coupons of a

total of 374 listed public debt securities. The data used for estimating zero coupon yield

curves cover three categories of government issues : 10-year JGB2, 3-month treasury bills

(3m TB) and 3-month financing bills3(3m FB). In order to obtain a more homogeneous

set of data, TB and FB prices are adjusted for withholding tax which is levied at issuance

and repaid at redemption4. The number of debt securities available for each month varies

considerably until the end of 2000, between 48 and 89 and it grows sharply from 2001 to

vary only between 86 and 95. In order to select the most accurately priced bonds, we apply

several data filters. We eliminate the data for 10-year JGBs with remaining maturity of less

1From 1985 :02 to 2001 :12 data are provided by TSE while from 2002 :01 to 2009 :10 data are

provided by JSDA.2The 10-year JGBs are most liquid bonds in the Japanese bond market, and they work as bench-

marks for bond investors.3The issue of 3-month TB was terminated in 2000 :03 while maturity of financing bills (FB) is

extended to three months in 1999 :04. As for three-month yield, we treat therefore 3-month TB and

3-month FB interchangeably.4Prices are adjusted according to this formula : Padj =

P·FF+(F−P0)·t

, where P represents the market

price, Padj the tax-adjusted market price, F the face value, P0 issue price and t the tax rate (0.18).

128

than half a year not only because of the existence of a redemption fee5 but also because

these JGBs are not actively traded and appear to be significantly influenced by their low

liquidity and therefore they are not accurately priced. Moreover, in order not to distort “real”

yields we eliminate outliers from data set bonds. We exclude then bonds whose yields differ

greatly from these at nearby (similar) maturities.

3.2.2 Estimation procedure

Our objective is to estimate macro-dynamic yield model from the spot rates. In order to

estimate the spot rate curve, given a set of bond prices, we apply the B-spline method and

parametrize the spot curve in terms of a cubic B-spline. Moreover, we apply smoothing

splines that incorporate a roughness penalty parameter, as the Bank of Japan approach and

use Fisher et al. (1995) method to estimate spot rate curve. The advantage of using this

method is that the cubic B-spline has base functions which have compact support (non-zero

function value) in a knot interval, and we can impose non-negative spot rate restriction in

the estimation procedure. This is even more important when using Japanese data since, in

the special case of the zero-interest-rate environment characterizing the Japanese economy

since 1999, short maturity bond yields can be negative6.

Given the wealth of literature detailing the use of smoothing cubic spline to extract

spot rates, we provide only the essential elements of the method7. Consider a set of N bonds

traded on one date. Let Pi be the market price of bond i ,Ci ,j

1≤j≤ki

be its principal and

interest payment, which is paid at a set of cash flow dates ti ,j1≤j≤ki . Under the classical

5Bonds with a remaining time to maturity below half a year are usually excluded to estimate yield

curves using Japanese government securities. This because the redemption fee could lead to negative

yields. This issue doe not impose problem in our work since we impose positive spot rate constraint

in estimation procedure, which is explained below.6An other solution consists of replacing negative values of yields with zeros as in Ueno et al.

(2006) that combine Black model with Gorovoi and Linetsky (2004) model to estimate yield curve

where interest rates are considered as options and negative values are replaced by zeros.7See Fisher et al. (1995) for more complete description of this model.


assumptions that there is no taxes or transaction costs, absence of arbitrage implies that

the observed bond price (market price plus accrued interest ai) is equal to the present value

of its future cashflows :

Pobsi = Pi +ai = Pi + ǫi =ki

∑j=1

Ci ,jδ(ti ,j)+ ǫi (3.1)

=ki

∑j=1

Ci ,j exp(−ti ,jζ(τi ,j))+ ǫi (3.2)

=ki

∑j=1

Ci ,j exp(−

∫ ti ,j0f (s)ds)+ ǫi (3.3)

where δ(.) is the discount function, ǫi are independent and normally distributed with mean

of zero and variance σ2. It is widely known that the discount function δ(t) is related both

to the spot rate (ζ(t)) and to the instantaneous forward rate (f (t)) respectively by ζ(t) =

− ln(δ(t))/t and f (t) =−δ(t)′/δ(t).

When using the smoothed cubic B-spline we place the B-spline bases on the spot

rate curve :

ζ(t) =κ

∑τ=1

βτφτ (t) (3.4)

where φ(t) = (φ1(t),φ2(t)...φκ(t))′ is a cubic B-spline basis, it is an κ-dimensional vector

constructed from a set of basis functions (φj(t); j = 1, ...,κ), and β = (β1, ...,βκ) is an

unknown parameter vector to be estimated and κ is the number of knot points plus 2.

We impose positivity constraints in the estimation of the spot rate curve. Since the cubic

B-spline basis functions take their maximum at a center of adjacent knot points, it suffices

to verify positiveness of the sport rates at middle points of adjacent knot points so as to

assure positive spot rates. Therefore, the spot rate curve is chosen to be the cubic B-spline

130

which minimizes the objectif function for a given λ, with respect to β as follows :

Min

βN

∑i=1

(Pi − Pi(β))2+λ

∫ T0ζ′′(s)ds

SC ζ(t)≥ 0(t ∈ [0,T ])

(3.5)

where T is the maximum maturity. The first term measures the error in the pricing of input

bonds. The second term represents the roughness penalty parameter that sets the level of

smoothing in term structure. It is the size of the roughness penalty parameter (λ) that

controls the tradeoff between the smoothness in the curve and the goodness of fit. The

value of λ is determined by Generalized Cross-Validation (GCV), it is chosen to minimize

the expression

γ(λ) =∑Ni=1(Pi − Pi(β

∗(λ)))2

(N− θenp(λ))2(3.6)

where enp(λ) is the effective number of parameters, θ the cost or tuning parameter. For

each λ we solve β∗(λ) and then calculate γ(λ). In this paper we impose non-negativity

constraint when estimating β to guarantee the positiveness of the spot rates8, otherwise

the estimated spot rates may be negative at shorter maturities. In the Japanese case this

problem could arise as short term interest rates are almost zero starting from 1999.

As for the number of knot points selected here, Fisher et al. (1995) suggest choosing

the number of knot points to be roughly one third of the sample size. Applied to Japanese

bond market this gives us 15 knot points. It is also necessary to decide on their location ;

although the knots could be distributed evenly over time to maturity it is common to concen-

trate them towards the short end to capture the greater complexity of the curve at shorter

maturities. The maturities at which our knots are located are 0, 0.25, 0.5, 0,75, 1, 2, 3, 4,

5, 6, 7, 8, 9 and 10 years. The size of the roughness penalty parameter (λ) depends on that

of the tuning parameter θ, which is fixed by discretion. If this parameter was almost equal

8The estimates are made using Matlab software.


to zero there would be no smoothing of the curve and the resulting forward curve could

oscillate wildly. Alternatively, if it was large, the estimated forward curve would be smoother

at the expense of goodness of fit. We follow BIS (2005) and use a tuning parameter of 3.

3.2.3 Summary statistics

Figure 3.1 provides a composite picture of Japanese Government Zero-coupon bond yield

curves over the sample period between 1985 :02 and 2009 :10. It shows that during the

period of quantitative easing, from March 2001 to March 2006, yields for very short-term are

zero. The non-negativity constraint prevents from possible negative yields for these years.

Figure 3.1 – Japanese Government Bond spot curves 1985-2009

Note : Spot rates are estimated using Fisher et al. (1995) model where we impose

positive constraint. Sources : data on prices and bond specifications are provided by

both TSE and JSDA.

In oder to summarize the statistical properties of the estimated zero-coupon yields

over the sample period, we focus on the 1-, 5- and 10-year spot rates as representative short-

132

, medium- and long-term interest rates. We define the level, the slope and the curvature as

13

(y(12)t + y

(60)t + y

(120)t

), y(12)t − y

(120)t and 2y

(60)t − y

(120)t − y

(12)t , respectively. Table 3.1

provides a summary of the descriptive statistics on the four measures of the spot rate curve.

The downward level shift and the reduction in volatility of spot rates are apparent, especially

at the long end of the curve. The average spot rate curve is upward sloping, meaning

that spot rates rise as the maturity of bonds lengthens ; standard deviations of spot rates

generally decrease with maturity ; and spot rates are highly autocorrelated, with decreasing

autocorrelation at longer maturities.The spot rate levels show mild excess kurtosis at short

maturities which decreases with maturity, and positive skewness at all maturities. Excess

kurtosis is, however, more pronounced for first-differenced spot rates (for example, 4.145

for the 1-year spot rate). We reject the normality hypothesis at the 5% level for all measures

of the spot rate curve. These descriptive statistics are consistent with some stylized facts

in bond pricing.

Table 3.1 – Descriptive statistics : Japanese spot rate curves

Central moments Autocorrelations

Mean Std.Devn Skewness Kurtosis J-B test Lag 1 Lag 2 Lag 3

Levels

1-year 0.0192 0.0193 1.5282 1.3529 0.000 0.9602 0.921 0.873

5-year 0.039 0.024 0.964 0.333 0.000 0.968 0.94 0.907

10-year 0.0507 0.0165 0.8649 0.2194 0.000 0.930 0.881 0.83

Slope -0.0315 0.0102 0.6199 0.4957 0.000 0.831 0.70 0.613

Curvature 0.0052 0.0098 0.9708 0.8497 0.000 0.853 0.778 0.71

1st-differences 1-year -0.000339 0.003503 -0.396 4.145 0.000 -0.0161 0.228 -0.171

5-year -0.0004 0.0036 0.663 1.99 0.000 -0.016 -0.011 -0.123

10-year -0.000177 0.0059 0.333 2.861 0.000 -0.1647 -0.295 -0.286

Slope -0.00016 0.0054 0.322 1.704 0.000 -0.137 -0.105 0.099

Curvature -0.00015 0.0051 -0.927 5.407 0.000 -0.26 -0.126 -0.034

3.3. Yield-Curve Fitting : The Macro-Finance Model 133

3.3 Yield-Curve Fitting : The Macro-Finance Model

3.3.1 Methodology and Estimation

The recent macro-finance literature has convincingly advocated the case for the

existence of a bi-directional link between the term structure and the rest of the economy

(Ang and Piazzesi (2003), Evans and Marshall (2007), Diebold et al. (2006) and Rudebusch

and Wu (2008)). Moreover, as documented in several papers for the US and UK9, there is

strong evidence of instability in the dynamics of the yield curve. What makes the Japanese

case particularly interesting is that its economy has become very unstable, having experienced

significant institutional and monetary strategy changes during the “lost decade”. However,

even though earlier empirical contributions based on Japanese data (Oda and Ueda (2007),

Okina and Shiratsuka (2004a), Baba et al. (2005) and Ugai (2007)) show that macro

variables have a clear and significant lowering effect on yield curve, they do not examine the

potential reverse influence from the yield curve to the real economy. In addition, to the best

of our knowledge no study has yet tried to model time variations in both the yield curve and

the economy, simultaneously for the Japanese case.

The model presented here is proposed by Bianchi et al. (2009) ; it is set in state-

space form and can be seen as a time-varying extension of the approach used by Diebold

et al. (2006) and developed by Nelson and Siegel (1987). In particular, it allows for time

variation in the state equation, thus revealing possible recurring structural breaks in the time

series dimension of the underlying yield curve factors.

The observation equation is a description of the phenomenon that is being investi-

gated. In the case of the Nelson–Siegel model, the observation equation would be the yield

curve equation. The state equation is a description of how the underlying factors evolve over

9Prominent examples include Diebold et al. (2006), Bianchi et al. (2009), Rudebusch and Wu

(2008).

134

time ; these factors are taken to be unobserved and will be estimated using the Kalman fil-

ter. The following equations represent the Nelson–Siegel model written in state-space form.

First, the observation equation :

yt(τ) = Lt +

(1−e−τλ

τλ

)St +

(1−e−τλ

τλ−e−τλ

)Ct +et (τ) (3.7)

where yt(τ) is the bond yield to maturity τ at time t ; Lt , St and Ct are the unobservable

level, slope and curvature factors of the yield curve, the factor loadings 1,(1−e−τλ

τλ

)and

(1−e−τλ

τλ −e−τλ)are for level, slope and curvature factors, respectively. The unity coefficient

is a constant, so that it does not decay to zero in the limit. The loading of St is an exponential

function that starts at one and decays monotonically towards zero. The loading of Ct starts

at zero, increases with the maturity τ and then declines approaching zero. λ is a parameter

that governs the exponential decay and determines for which maturity the function assumes

its maximum. We calculate λ10 using Japanese data and we set it equal to 0.04215. This

implies that the curvature factor loading reaches its maximum at a maturity of 30 months.

Following Bianchi et al. (2009) we assume that the idiosyncratic component et (τ) is serially

correlated and heteroskedastic but uncorrelated across maturities E(et (i)

′et (j)

)= 0 for

i 6= j . In particular : et (τ) = ρ(τ)et−1 (τ)+ψ1/2t (τ)ǫt where the volatility ψt (τ) follows a

geometric random walk log(ψt) = ln(ψt−1 (τ))+ωt . Second, the state equation describes

the dynamics of these factors as a time-varying VAR :

Zt = αt +P

∑p=1

βt,pZt−p+ vt (3.8)

where the n×1 vector Zt = [Lt ,St ,Ct ,πt ,Yt ,Rt ]′ denotes macro and yields data matrix. The

errors vt are assumed to be normally distributed with 0 mean and time-varying covariance

matrix Ωt . Following Primiceri (2005) and Cogley et al. (2005), we use a triangular reduction

10Diebold et al. (2006) choose τ = 30 months as a reference maturity for the “medium term” and

set λ equal to 0.077 for US data.


of the state error covariance as follows :

Ωt = A−1t ΣtΣ

′

tA′−1t (3.9)

where At is a lower triangular matrix with ones on the main diagonal and Σt is a diagonal

matrix. the time-varying matrices Σt and At are defined as follows :

At =

1 0 · · · 0

a21,t 1. . .

...

... · · ·. . . 0

an1,t. . . an(n−1),t 1

and Σt =

σ1,t 0 · · · 0

0 σ2,t · · · 0

... · · ·. . .

...

0 · · · · · · σn,t

(3.10)

The vectors at =[a21,t ,(a31,ta32,t), · · · ,(an1,t · · ·an(n−1),t)

]′are the equation-wise stacked

free parameters of At and ht = log(diag(Σt)). As suggested by Primiceri (2005) and Bianchi

et al. (2009) among others, all parameters are assumed to be independent random walks11 :

φt = φt−1+ηφt

at = at−1+ ǫt

hi ,t = hi ,t−1+ηht

(3.11)

where φ= [αt βt,p].

11As explained in Primiceri (2005) the random walk assumption has the advantages of focusing

on permanent shifts and reducing the number of parameters in the estimation procedure. However,

a random walk model is non-stationary and it is obviously "more explosive" than the number of

observation increases. Our sample contains no more than 200 time series observations. Using such

a short period alleviates this problem.

136

The variance-covariance matrix of innovations is block-diagonal :

ωt

vt

ηφt

ηht

ǫt

∼ N(0,V ), where V =

R 0. . . 0

0 Ω. . .

...

... Q. . .

...

... G 0

0 · · · 0 S

(3.12)

where G = diag(ς21 , ... , ς2n). For simplicity, it is assumed that the matrix S is also block-

diagonal with respect to the parameter blocks belonging to each equation.

On the other hand, Zt contains a set of unobservable factors of the yield curve next

to observable macroeconomic factors, namely, the output gap (Yt), inflation (πt) and the

monetary policy instrument (Rt). As noted by Diebold et al. (2006), the intuition behind

this ordering is the fact that the yield curve observations are dated at the beginning of the

month. Under this identification scheme, yield factors are assumed to be contemporaneously

unaffected by the macro factors.

To estimate the model we use the procedure originally suggested in Kim and Roubini

(2000) and used by Bianchi et al. (2009), whereby we employ the Gibbs sampling algorithm

that exploits the fact that given observations on Zt , the model is a time-varying parameter

model. In this paper preference is given to the Bayesian one-step method rather than the

two-step Diebold-Li approach because in the two-step approach the uncertainty of parameter

estimation and signal extraction in the first step may affect the second step computations.

However, simultaneous estimation of all parameters results in correct inferences. We follow

the same basic steps of the algorithm as in Bianchi et al. (2009) whereby, first, given initial

values for the factors, the VAR parameters and hyperparameters are simulated. Second, given

data on Zt and y(τ) and a value for λ, the variance and covariance matrices are simulated.

Third, conditional on all simulated parameters, factors are simulated again. Finally, the


chain is started again, going back to the first step. This iterative procedure converges to an

invariant density that equals the desired posterior density. Gibbs-sampling is implemented in

such a way that the first 45,000 draws in the Gibbs simulation process are discarded, then

the next 5,000 draws are saved and used to calculate moments of the posterior distribution.

3.3.2 Priors

We follow Primiceri (2005) and calibrate some of the prior distributions using a training

sample and estimating a time-invariant VAR model by OLS. To initialize the factors and

calibrate priors for the VAR, a pre-sample of three years starting in February 1985 is used.

Therefore, the results presented in the following section refer to the period February 1988-

October 2009. The remaining prior distributions are also chosen in a manner similar to

Primiceri (2005) and Bianchi et al. (2009). The prior choices can be summarized as :

φ0 ∼ N(φOLS ,Var(φOLS))

A0 ∼ N(aOLS ,Var(aOLS)

hi ,0 ∼ N(Logµ0, In×10)

logψ0 (τ)∼ N(logµ0 (τ) , In×10)

Q0 ∼ IW(Var(φOLS)×10−5,T0

)

Si ,0 ∼ IW (Si ,Ki)

ς2i ∼ IG

(10−4

2,1

2

)

Ri ,0 ∼ IG

(10−4

2,1

2

)

(3.13)

where T0 is a training sample. µ0 are the diagonal elements of vOLS , which is the OLS

estimate of the VAR covariance matrix estimated on the training sample data. aOLS denotes

the off diagonal elements of vOLS . i = 1, ... ,n indexes the blocks of S . Si is calibrated using

138

aols . Specifically, Si is a diagonal matrix with the relevant elements of aols multiplied by

10−3. Note that factor priors are obtained using the least squares estimator employed by

Diebold et al. (2006) and thus et (τ) is obtained using the initial least squares estimates of

the factors.

3.4 Empirical results

3.4.1 Preliminary Empirical Results

For the empirical analysis, we use data on the following three variables : the output gap12, the

collateralized overnight call rate as an indicator of monetary policy ; and inflation, measured

as annualized monthly changes in the consumer price index excluding fresh food. To obtain a

parsimonious specification, we choose a lag order of one, as the computations are otherwise

very burdensome.

We start the analysis by examining the estimated factors which are displayed in Figure

3.2 together with their empirical proxies defined above. In order to evaluate the uncertainty

characterizing the factors Figure 3.2 also plots the 16-th and 84-th quantile intervals of

the standard deviation. In our case, the error bands are almost indistinguishable from the

estimated series, indicating that factors are precisely estimated. However, when it comes

to their empirical counterparts, the fit is at best satisfactory. Correlations between the level

factor and the slope factor and their empirical counterparts are 0.76 and 0.82, respectively.

Even worse, the estimated curvature does not fit its empirical proxy well. This indicates that

level and slope factors can be approximated by their corresponding empirical counterparts

whereas approximations of the curvature factor are rather difficult. It is interesting to note

that estimated factors start to wander from their empirical counterparts from about 1992

12The output-gap data, prepared and provided by the BOJ staff, are related to paper of Hara et al.

(2006)


and to catch up with them again from 1999. We point out that this finding is not different

from that of previous work as in Tam and Yu (2008).

Figure 3.2 – Estimated factors and their empirical counterparts

Note :In the top panel, together with factors (blue lines), the graph shows the 68% probability

bands (red band) and empirical counterpart level, slope and curvature (dotted lines).

We recall that the macroeconomic interpretation to factors are as follows : the level

factor is typically associated with some measure of long-run expectations of inflation13. The

slope factor contains information about the expected stance of monetary policy and thus is

a predictor of future economic activity and the curvature factor could also be informative

about the evolution of the economy.

The error volatility has gained increasing prominence in macro-finance models. Our

results support our choice of the heteroskedasticity assumption. Figure 3.3 plots the esti-

mated diagonal elements of the time-varying covariance matrix. The estimated stochastic

volatility of the structural shock both to factors and to the macroeconomics variables shows

that the time variation of volatilities has been significant. Therefore, using the homoskedas-

ticity assumption would result in biases in the covariance matrix for the disturbances. The

time-varying volatilities of the level-and-curvature-factor shocks display a stable declining

path. The volatilities of the shocks to the slope and curvature factors drop to close to zero

during the ZIRP and QEMP periods. The stochastic volatility of the shocks to inflation

13It is particularly difficult to compare this factor with actual data, as information on long-run

expectations is not available for Japan. Inflation-index 10-year bonds were only introduced in March

2004

140

declines during the same period. We observe some short-lived increases in shocks to the

output gap. The largest increase occurred around the global financial crises of 2008. The

volatility of the shock to the call rate is typically of the evolution of Japanese monetary

policy. Call rate volatility declines up to 1995 to near zero and disappears afterwards. This

reflects the decline of the call rate to 0.25% in 1995 and then within ±0.25% in 1998 be-

fore the implementation of the ZIRP and the QEMP when the monetary policy instrument

shifted from the call rate to current account balances.

Figure 3.3 – Estimated Standard deviation of the FAVAR residuals

The figures show posterior means of the estimated standard deviation of the structure shocks.

The solid lines show the median and the band areas represent the 68% bands.

3.4.2 Evidence on the expectations hypothesis (EH)

Empirical evidence has recently challenged the validation of the EH using Japanese data.

This issue is especially important for the Japanese economy because the principal channel

suggested by either ZIRP and QEMP is the expectation channel. One important channel

through which monetary policy works is long-term interest rates, shaping them so that in turn


Figure 3.4 – Extracted expectation component

EH consistent yields (solid lines) together with their actual counterparts (dashed lines). Solid

lines show the median and the band areas represent the 68% bands.

they affect the level of economic activity. The expectation that a policy of low short-term

interest rates may be maintained for a substantial period of time will likely lower medium- to

long-term interest rates. The crucial link between a central bank’s instrument and long-term

interest rates is the EH of the yield curve theory. However, the empirical support for the EH

and the effectiveness of the policy commitment is rather mixed. Thornton (2004) applies a

bivariate VAR for long-term and short-term interest rates for the period from March 1981

to January 2003. He shows that the EH does not hold for the Japanese case. The author

also argues that one reason why EH fails is because the term premium varies over time.

In this chapter we review the validity of EH and the effects of the BOJ’s expectations

management on the JGB yield curve using a more flexible approach. Using a time-varying

coefficient model we can check whether EH consistent yields track actual yields well or not.

142

However, examining the evidence on EH requires separating expectations of future interest

rates from the term premium in the term structure. According to the EH, a long bond yield

is the average of the expected short-term rates :

yt(τ)EH =

1

τ

τ−1

∑i=0

Etyt+i(1) (3.14)

On the other hand, the interest rate risk means that investors could require additional

compensation, and EH ignores this risk. The term premium, therefore, refers to this com-

pensation and any other deviation from the EH :

TP(τ) = yt(τ)− yt(τ)EH (3.15)

The implicit assumption behind our time-varying coefficient VAR model is that agents review

their beliefs about uncertainty regarding inflation, real activity and monetary policy at each

period. This assumption allows us to perform accurate predictions since it makes the model

more flexible and more realistic. In addition, the term premium could vary with the business

cycle, as investors might be more risk-averse in recessions than in booms. Figure 3.4 provides

some selected maturity EH consistent yields together with their actual counterpart. The

theoretical yields tracking actual yields well, despite limited deviation which occurred between

1992 and 1998, indicate that the expectations hypothesis of the term structure of interest

rates is generally supported even during the QEMP period.

3.4.3 Time-varying term premium

According to the expectations hypothesis the term premium should simply be a function of

maturities, but not a function of time. However, the empirical investigation of the expecta-

tions theory has been unsuccessful, and the hypothesis has almost always been rejected. The

particular case of Japan confirms this result (Thornton (2004)). One possible explanation for


the empirical failure of the EH is the presence a time-varying term premium. Time-variation

in term premia might arise because of changes in market participant’s preferred risk aversion.

Moreover, a standard finding in the literature is that term premia are countercyclical ; they

seem to be highest during and immediately after recessions and lowest in booms (see, for

example, Cochrane and Piazzesi (2005)).

Figure 3.5 – Estimated term premium

The solid lines show the median and the blue areas indicate the 16th and 84th percentiles of

the term premium.

Estimated term premia of some selected maturities are shown in Figure 3.5. There

appears to be a structural break in the behavior of the term premium for all maturities– in

particular, volatility and magnitude have subsided since 1998. The particular episode of large

decrease in term premia between 1989 and 1992 may be explained by the heavy demand for

JGBs during the burst of the asset price bubble. A second episode of positive term premia

coincides with Japan’s economy stagnation from 1992 to 1999. When real GDP showed a

144

slight recovery phase in 1999, term premia declined and even started to fluctuate around

zero over the rest of the sample for longer maturities. It is interesting to note that during

the quantitative easing period between 2001 and 2006 term premia decline to a lower level.

This could be due to the heavy demand for JGBs from the BOJ during that period. Another

explanation of the decline of term premia is that it could reflect a global decline in risk

and term premia in developed and emerging market economies at that time. Shin and Shin

(2010) argue that global banking sector liabilities due to foreign creditors, called “non-core

liabilities”, increased rapidly from 2003 up to the financial crisis in 2008. The authors show

that the banking sector relies more on funding from foreign creditors when global economic

conditions are favorable since private sectors’ deposits are not enough to sustain banks’

desired balance sheets expansions. These additional funds are invested in a variety of assets

such as corporate and government securities, lowering yields and risk premia in these assets.

3.4.4 Empirical Results From the Macro-Finance Model

3.4.4.1 Are monetary policy shocks an important source of variation in the

yield curve ?

Given the focus of this chapter on the effects of monetary policy on the yield curve, the

discussion is focused on the decomposition of the unconditional variance of selected endo-

genous variables into contributions from the monetary policy shocks14.

The variance decomposition of the call rate15, shown in Figures 3.6, identifies the

contribution of the monetary policy shock to variations in the yield curve and macroeconomic

14The proportion of the unconditional variance accounted for by monetary policy shock is calculated

as the ratio of the unconditional variance due to the shock of interest rate and the total unconditional

variance. For more details see Bianchi et al. (2009).15Using call rate as monetary policy instrument during the QEMP may be subject to criticism

since the BOJ is targeting the monetary base. But, as demonstrated in Nagayasu (2004), the BOJ

had to practice a strong smoothing of interest rates in order to keep the short-term rate near to

zero during this period.


variables. The variance decomposition implies that a variation in the call rate is largely due

to independent monetary policy shocks. However, during the QEMP period (between 2001

and 2006) the contribution of the policy shock is negligible. This is consistent with the

change in monetary policy instrument from call rate to current account balances by the

adoption of the quantitative easing strategy. After 2006 the contribution of monetary policy

shocks to the policy interest rate becomes again important. The monetary policy rate shock

accounts at most for 20% of the fluctuations in output until 1995, around the time when

the BOJ dropped its interest rate to 0.5%. The contribution has been negligible afterwards.

Innovations to call rate makes the largest contribution to the variance of inflation during

the period between 1988 and the end of 1994, after which it veers to almost zero. A similar

patern emerges in its contribution to the fluctuations in level, slope and curvature factors.

Figure 3.6 – Unconditional variance - Call rate shock.

The figures show the variable contributions to the monetary policy rate variation. The solid line

denotes the median estimate, while the band indicates the 16% and the 84% quantiles of the

posterior distribution of variance decompositions.

146

Looking at the variance decomposition of inflation shocks, displayed in Figure 3.10,

it is apparent that inflation does not explain a large part of yield curve factors. The largest

contribution was in the early 1990s after which it became negligible. However, Figure 3.11

shows that variances in level and slope factors are significantly explained by the variance in

the output gap until 1994, which accounts for approximately 30% and 40%, respectively.

This indicates that news about the future evolution of output might be more important for

the dynamics of the yield curve than inflationary concerns for that period. This contribution

quickly shifted to very low levels over the period between 1995 and 2006 after which it rose to

similar levels as before. Altogether, these results suggest a negligible role of macroeconomic

variables in influencing the yield curve during the long-lasting economic stagnation between

1995 and 2006.

Figure 3.7 complements the variance decomposition by displaying the impulse res-

ponse functions of the yield-curve factors and the macroeconomic variables to a monetary

policy shock16. We recall that the ultimate objective of the Japanese monetary policy is to

affect the yield curve level in order to stimulate the economy and to achieve low and stable

inflation. More precisely, we focus on the effectiveness of the QEMP in affecting the long-

end of the yield curve. Before turning to the impulse responses following a surprise change

of the monetary policy rate, it is worth calling attention to the difficult interpretation of the

level factor reaction. Indeed, the success of monetary policy could be defined as a decrease in

the long-end of the yield curve via either expected short-term rates (policy-duration effect),

term premium (portfolio-rebalancing channel) or both of them. However, this represents only

an intermediate target in an attempt to generate economic recovery and to stop deflation.

Therefore, the definition of a successful policy may be subject to criticism since economic

recovery, the final goal of the BOJ, is expected to increase inflation expectations and thus

16For the sake of brevity, we only report the impulse response functions of the key variables to a

chock to monetary policy. Results of impulse response functions to shock to the slope, curvature,

output and inflation are available upon request from the authors.


future short-term interest rates, which in turn will raise long-term interest rates. As argued

by Nagayasu (2004) monetary policy mechanisms take one to two years to achieve their

full effects. It seems appropriate, at the time of writing, to expect that the effectiveness of

QEMP, if any, would result in an increase of the level factor.

Figure 3.7 – Impulse responses - Call rate shock

The figure shows the reactions of inflation, output and level factor to a shock to the call rate over

25 months for three sample periods. The solid lines show the impulse responses implied by the

time-varying VAR following a rise by 100 basis point in call rate. The impulse responses in each

sub-sample are average of the impulse response in each month in that sample. The band areas

represent 68% error bands.

We report the responses for four sub-periods covering different main measures imple-

mented by the BOJ to stimulate the economy. The period between 1988 and 1994 represents

the period of high short term interest rate. The 1995-2001 period covers the so called tran-

sition period and the ZIRP period. The third and fourth sub-periods represent respectively

the QEMP period and after the exit from this strategy.

Consider the reaction of key variables to a monetary policy shock. Impulse response

148

functions of the level factor indicate a significant and persistent increase of the level factor

during the 1988-1995 period. During that period short-term interest rates were still high

and had a potent dynamic effect on the level of the yield curve. However, during the period

of low interest rates, specially the QEMP period, the effect of the call rate on the level

factor becomes insignificant. This finding is consistent with previous research (Okina and

Shiratsuka (2004a), Nagayasu (2004) and others) and corroborates the idea that under

the zero lower bound, and according to the expectation hypothesis, the expected future

short-rate becomes equal to zero and then the long-term interest rate becomes equal to

the expected future term premium. In this case it becomes more difficult for the central

bank to influence long-term interest rates. However, while statistically insignificant, the

decline in the level factor, which is more pronounced during the QEMP period, may reflect

the strengthening credibility of the BOJ and thus the effectiveness of its policy. Indeed, as

argued in Diebold et al. (2006) and Bianchi et al. (2009), if monetary policy is credible the

level factor, everything being equal, should fall after a positive shock to call rate, because

the expectation of future inflation declines. Since the BOJ commits itself during the QEMP

period to maintaining the short-term rates to a zero level, the decline in the level factor after

an increase in the call rate is by analogy equivalent to a rise in this factor to a monetary

policy expansion. This can be due to an expectation of an economic recovery and an inflation

rise, indicating a monetary policy success.

The reaction of inflation suggests a strong evidence of a price puzzle regardless of the

sample period17. The magnitude of the positive response is smaller over the high-interest-

rates period (1988-1995) ; the inflation response veers to zero and becomes insignificant

more rapidly. The call rate shock still has a negative effect on the output gap with a more

persistent response over the periods before 1995 and after 2006.

17The price puzzle problem can be due to the lack of information included in the VAR system as

explained in Bernanke et al. (2005), or to the small number of lags chosen given the model complexity


3.4.4.2 Is there a feedback effect from the yield curve to macroeconomic

variables ?

The variance decomposition of the level factor, shown in Figure 3.8, implies that the shock to

the level factor has the strongest impact on the long end of the yield curve. During the period

between 1995 and 2006 level shocks account for more than 90% of all level-factor variation.

This suggests a large amount of idiosyncratic variation in the long end of the yield curve

that is unrelated to macroeconomic fundamentals. This shock explains at most 5% of the

variance in inflation and 10% of the variance in the output gap. The increasing contribution

Figure 3.8 – Unconditional variance - level factor shock

The figures show the variable contributions to the level factor variation. The solid line denotesthe median estimate, while the band indicates the 16% and the 84% quantiles of the posterior

distribution of variance decompositions.

of this shock to the call rate since 1995 is matched by the contribution of the slope factor.

These results reflect the increasing emphasis on long-term interest rates by the monetary

150

policy stance during the quantitative easing period. Taken together, financial shocks do not

explain much of the variance in macroeconomic variables. These results are not surprising

in the case of Japan as the relation between the yield curve and macroeconomic variables

largely depends on financial system conditions. As argued in Koo (2008) corporate sector

was busy paying down debt to improve its balance sheets, which were destroyed following

the asset price collapse. Then, corporate sector was reluctant to borrow new loans or to

issue new bonds for new investments. In this case real activity reacts less to the financial

markets shocks, in particular to the yield curve shocks.

Figure 3.9 summarizes impulse response functions to an unexpected increase of the

level factor. A positive surprise change of the level factor indicates an increase of inflation

during the period before 1995 and after 2006, but this effect remains insignificant. However,

by contrast with the conventional wisdom, in both periods of low interest rates, including

the QEMP period, inflation decreases immediately in reaction to the level factor shock and

reverts towards zero. The level shock has a positive effect on output gap, although its impact

seems to exhibit time variation. During the period between 1995 and 2006 output gap reacts

significantly reinforcing our finding that the contribution of macroeconomic variables to level

factor variation, if any, comes from output gap.

Figure 3.14 plots responses to an unexpected positive change of the slope factor. An

increase in the slope factor means a reduced spread between long-term and short-term bonds,

which indicates a monetary policy tightening and thus a decline of economic activity18. The

direction of the reaction of the output gap corroborates this view, while its responses are

short-lived and hardly significant. The reaction of inflation looks qualitatively similar to the

response to a level shock. An unexpected increase of the slope factor is followed by an initial

decrease of inflation for all sub-samples. The call rate rises after the slope shock but its

18Normally a decreasing yield curve slope announces an economic slowdown. But since the loading

of the slope factor in our model decreases with maturity and corresponds to the difference between

short- and long-term yields, an increase in this factor corresponding therefore to a decrease in the

term spread.

3.5. Conclusion 151

reaction is not significant except during the first period of high interest rates. Altogether,

the impulse response functions reinforce results from variance decompositions that the yield

curve is not informative about macroeconomic variables when interest rates decline to a

very low level and specially during the quantitative easing period.

Figure 3.9 – Impulse responses - Level shock

The figures show the reactions of inflation, output gap and call rate to a shock to the level factorover 25 months for three sample periods. The solid lines show the impulse responses implied by

the time-varying VAR following a rise by 100 basis point in call rate. The impulse responses in

each sub-sample are average of the impulse response in each month in that sample. The band

areas represent 68% error bands.

3.5 Conclusion

This chapter has examined the effects of the quantitative easing strategy in Japan on

the yield curve and the possible feed-back effect by applying a macro-finance model that

allows for time-varying parameters. This model provides maximum flexibility in measuring

the effect of macroeconomic variables on the yield curve and vice-versa. It incorporates three

152

macroeconomic variables, namely the output gap, inflation and the call rate, and three yield

curve factors which represent level, slope and curvature, summarizing the term structure

of interest rates. Before estimating this model, we constructed a database of zero-coupon

yields using Japanese government bond price data for the period between 1985 and 2009.

In order to estimate the spot rate curve we applied the smoothing B-splines method and

imposed non-negative spot restrictions in the estimation procedure. We thus avoid having

negative spot rates during the lower short-term interest rate period.

Contrary to the results of standard term-structure models with a time-invariant term

premium, our results show that the expectations hypothesis of the term structure of inter-

est rates is generally supported, even during the QEMP period. Empirical results from a

macro-finance model show that the relationship between the macroeconomic and financial

variables has changed significantly over time. There is hardly any relationship at the zero

lower bound on interest rates and deflation, and especially during the quantitative easing

period. The structural decomposition of the yield curve into its macroeconomic components

shows that, by contrast with conventional wisdom, inflation, activity and monetary policy

play a less prominent role in explaining the yield curve. They play no role at all particularly

during quantitative easing. The variance decomposition of the level factor indicates that the

main part of the variation in this factor comes from the yield curve factors, limiting the

contribution of macroeconomic variables. Conversely, the relative importance of yield curve

factors in the variation of inflation is relatively small and even inexistent during quantitative

easing. A more pronounced effect of yield curve factors on the output gap is detected du-

ring the period of high interest rates, and this effect disappears during quantitative easing.

Moreover, the increasing contribution of the level factor in the variation of the call rate

during quantitative easing reflects the increasing importance attributed by monetary policy

to long-term interest rates. These finding are corroborated by impulse response results.

As the objective of the BOJ during the quantitative-easing regime is to affect long-

3.5. Conclusion 153

term interest rates in order to stimulate the economy, a credible and successful expansionary

policy is a policy resulting in a rise in the level factor due to a recovery and inflation expecta-

tions. This is equivalent to a decline in the level factor following a positive shock to the call

rate. Impulse response analysis shows that during this period, while statistically insignificant,

the level factor declines in response to a positive shock to the call rate. This may be due to

an expectation of an inflation rise and an economic recovery, indicating increasing credibility

leading to monetary policy success.

On the other hand, the insignificant effect of the call rate on the level factor can

be explained by the fact that, at the zero lower bound of interest rates, expected future

short-term rates are almost zero and long-term interest rates become largely determined

by the forward term premium, which the BOJ finds difficult to influence. A focus on term

premium and expectation components would better capture the effect of monetary policy on

yield curve. In addition, according to Bernanke et al. (2005), simple variables alone cannot

represent economic concepts like activity or price. The alternative is then to use a model

with macroeconomic factors summarizing a larger set of macroeconomic variables. These

issues could well benefit from research attention in the future.

154

Figures

Figure 3.10 – Variance decomposition due to inflation

Unconditional variance due to the inflation shock. The graph shows the percentage of the variance

of each of the variables that explained by the inflation shock.

3.5. Conclusion 155

Figure 3.11 – Variance decomposition due to the output gap

Unconditional variance due to the output gap shock. The graph shows the percentage of the

variance of each of the variables that explained by the output gap shock.

156

Figure 3.12 – Variance decomposition due to slope factor

Unconditional variance due to the slope factor. The graph shows the percentage of the variance

of each of the variables that explained by the slope factor shock.

3.5. Conclusion 157

Figure 3.13 – Variance decomposition due to curvature

Unconditional variance due to the curvature factor. The graph shows the percentage of the variance

of each of the variables that explained by the curvature shock.

158

Figure 3.14 – Impulse response functions to slope shock

The figures show the reactions of level factor, inflation, output and call rate to a shock to the

slope factor over 25 months for three sample periods. The solid lines show the impulse responses

implied by the time-varying VAR following a rise by 100 basis point in call rate. The impulse

responses in each sub-sample are average of the impulse response in each month in that sample.

The band areas represent 68% error bands.

Conclusion générale

La décision de recourir à des politiques monétaires non conventionnelles afin de faire face

à la crise financière de 2008 fait l’unanimité au sein des principales banques centrales ; ces

politiques visent à stimuler les économies dans lesquelles les taux d’intérêt atteignent leur

niveau plancher à zéro. La Fed, en décembre 2008, a mené une politique dite « d’assouplisse-

ment des conditions de crédits » en se substituant aux banques et au marché financier pour

financer directement l’économie. Dans le même temps, la banque d’Angleterre, ainsi que la

BCE, ont adopté des politiques semblables consistant à acheter massivement des actifs à

long terme, jusqu’à atteindre une cible de taille du passif de leurs bilans. Plus récemment,

en octobre 2010, la banque du Japon a décidé de reprendre sa politique d’assouplissement

quantitatif menée entre 2001 et 2006 sous une autre forme (“Comprehensive Monetary Ea-

sing”), en mettant davantage l’accent sur l’achat des actifs à long terme. Face à la lenteur

de la reprise, la Fed décide en novembre 2010 d’enclencher une deuxième étape dans l’ap-

plication de sa politique non conventionnelle en annonçant l’achat des bons à long terme du

Trésor américain, dans le but de baisser les taux d’intérêt de long terme. Ces interventions

des banques centrales nous ont incité à questionner dans cette thèse l’efficacité de ce type

de politique monétaire au regard de l’expérience japonaise d’assouplissement quantitatif de

2001-2006. Les trois chapitres du présent travail apportent des éléments de réponse aux

interrogations suivantes :

• La politique d’assouplissement quantitatif, telle qu’appliquée par la BOJ, était-elle

efficace pour stimuler l’économie japonaise et l’extraire d’une situation de dépression

et de déflation ? D’après la BOJ, cette stratégie n’aurait fait que stabiliser les marchés

159

160

financiers et empêcher le prolongement de la spirale déflationniste, sans pour autant

relancer l’économie. Ce constat signifie-t-il que la BOJ aurait dû injecter davantage

de réserves aux banques ? Ou bien aurait-elle dû maintenir la même politique plus

longtemps ?

• Par quels canaux les effets de cette politique peuvent-ils être transmis à l’économie

réelle ? L’approche neo-wickselienne met l’accent sur le canal des anticipations. Elle

considère que l’économie japonaise est tombée dans une situation de trappe à liquidité.

Elle suggère donc que la banque centrale s’engage explicitement à maintenir le taux

d’intérêt nominal de court terme à un niveau bas pendant une période significative, afin

de pouvoir agir sur la courbe des taux. D’autre part, l’approche monétariste se focalise

sur le canal du rééquilibrage de portefeuille. L’augmentation de la base monétaire par

l’achat des titres à long terme a pour effet direct de baisser les rendements de ces

actifs. De plus, étant donnée l’imparfaite substituabilité entre la monnaie et les actifs

non monétaires, la liquidité additionnelle fournie par la banque centrale pousse les

agents privés à rééquilibrer leurs portefeuilles par l’achat d’actifs non monétaires, et

baisse ainsi leurs rendements.

Pour être en mesure de répondre à ces questionnements, nous nous sommes efforcés dans

ce travail de thèse d’utiliser les avancées les plus récentes de l’économétrie de la politique

monétaire pour les appliquer à l’expérience japonaise d’assouplissement quantitatif. Les éco-

nomètres ont pris conscience du fait que les banques centrales fondent leurs décisions sur une

multitude d’indicateurs économiques alors que les estimations économétriques des détermi-

nants et des conséquences de ces décisions utilisent des modèles vectoriels autorégressifs

(VAR), où seul un très petit nombre de variables agrégées est pris en compte, et qu’elles

souffrent dès lors de nombreux biais (énigme de prix, non-neutralité à long terme). D’autre

part, il paraît évident que faire l’examen de la politique monétaire japonaise sans tenir compte

des changements de régimes aboutit à des résultats biaisés, et mène à des recommandations


erronées.

Le premier chapitre de cette thèse a été consacré à l’examen de la capacité de l’as-

souplissement quantitatif à stimuler l’activité et à sortir le Japon de la spirale déflationniste

qui s’est installée suite au dégonflement de la bulle spéculative. Ce travail complète la lit-

térature existante en combinant la méthodologie de Markov-Switching VAR avec celle de

l’analyse factorielle en un modèle appelé MS-FAVAR. Ce modèle a non seulement rendu

possible l’introduction d’un grand nombre de variables dans l’analyse mais a aussi permis de

prendre en compte les changements de régimes qui caractérisent la structure de l’économie

japonaise de ces deux dernières décennies. Des facteurs communs ont été extraits des va-

riables liées à l’activité, aux prix et aux taux d’intérêt et ont été introduits dans le modèle

MS-VAR ; ils représentent des mesures de concepts économiques généraux comme l’activité

réelle et les prix.

Le modèle présenté dans le chapitre 1 nous a permis d’identifier et de dater le change-

ment de régime de la politique monétaire. Le deuxième régime, détecté en 1999, correspond

à la période de politique du taux d’intérêt zéro et à celle de l’assouplissement quantitatif. De

plus, l’absence du problème d’énigme de prix et de la non-neutralité de la monnaie dans nos

résultats montre que la prise en compte du maximum d’information dans l’analyse donne

des résultats en accord avec les prédictions théoriques. Ce chapitre a montré en particulier

que l’effet de l’assouplissement quantitatif sur l’activité et sur les prix, bien que transitoire,

est significatif. Ce caractère transitoire peut expliquer pourquoi la BOJ juge que l’impact de

cette politique sur l’activité réelle et sur les prix reste modeste. Durant la majeure partie de

la période d’assouplissement quantitatif, jusqu’en 2005, les firmes japonaises étaient réti-

centes à avoir recours à des crédits ou à émettre des obligations pour financer de nouveaux

investissements, car elles étaient préoccupées par le paiement des dettes accumulées suite

au dégonflement de la bulle financière. La politique d’assouplissement quantitatif ayant été

stoppée juste après la fin du paiement de ces dettes par les firmes, nous nous autorisons

162

à penser que le prolongement de cette politique d’assouplissement quantitatif aurait été

profitable, et nous en déduisons donc que la BOJ en aurait récolté les fruits si elle l’avait

maintenu plus longtemps.

L’objectif du deuxième chapitre était l’identification des canaux de transmission et

la mesure de leur ampleur. A l’aide d’un modèle FAVAR avec paramètres évolutifs dans le

temps (TVP-FAVAR) nous avons pu analyser la période d’assouplissement quantitatif d’une

manière précise. Les résultats obtenus confortent ceux du premier chapitre en démontrant

l’efficacité de l’assouplissement quantitatif sur l’activité et sur les prix. Le canal de rééqui-

librage de portefeuille s’avère jouer un rôle important dans la transmission des effets de

l’assouplissement quantitatif. En particulier, la réaction positive, à la fois de la consomma-

tion et de l’indice boursier, montre que l’impact de l’augmentation de la base monétaire a

été transmis par l’intermédiaire de l’effet de richesse. D’autre part cet effet de l’augmen-

tation de la base monétaire sur les anticipations des agents privés, bien que transitoire,

conforte la déclaration de la BOJ que l’engagement à maintenir les taux à de faibles niveaux

a pu stopper la spirale déflationniste, sans pour autant générer de pression inflationniste

conséquente.

L’apport du chapitre 3 est le prolongement de l’analyse du canal des anticipations

dans le cadre d’un modèle macro-finance reliant les variables macroéconomiques à la courbe

des taux. Nous sommes dans un premier temps parvenus à estimer la courbe des taux zéro-

coupon en utilisant des prix d’obligations d’État japonais. Puis, un modèle macro-finance

avec paramètres évolutifs dans le temps a été employé afin d’analyser les interactions entre

la structure par terme des taux d’intérêt et les variables macroéconomiques. La prise en

compte de l’évolution des anticipations des agents privés a permis de démontrer la validité

de l’hypothèse d’anticipations rationnelles, sans laquelle le canal des anticipations ne peut

guère fonctionner. L’étude de la décomposition de la variance et des fonctions d’impulsion

a montré la faible interaction entre les variables macroéconomiques et la courbe des taux


durant la période d’assouplissement quantitatif. Quant à l’analyse de l’effet de la politique

monétaire, la réaction du niveau de la structure par terme suite à un choc sur le taux

d’intérêt de court terme a mis en évidence le renforcement de la crédibilité de la BOJ dans

son engagement à maintenir des taux d’intérêt à de faibles niveaux. Ce résultat conforte

ainsi les conclusions concernant le canal des anticipations trouvées dans le chapitre 2.

L’apport de cette présente thèse centrée autour du cas du Japon est ainsi de mettre

en lumière des enseignements utiles à la compréhension des politiques monétaires non

conventionnelles récemment appliquées par les principales banques centrales. Le travail réa-

lisé montre la capacité de la politique d’assouplissement quantitatif à influencer l’activité

et les prix, efficacité conditionnée par une durée suffisante d’application, et met aussi en

évidence la complémentarité entre le canal de rééquilibrage de portefeuille et celui des anti-

cipations.

Le choix de la variable de politique monétaire demeure problématique pour le cas

du Japon. Partant de l’idée que la banque centrale peut agir sur le taux d’intérêt ainsi

que sur le marché des réserves, Bernanke and Mihov (1998) développent un modèle VAR

semi-structurel qui extrait les informations relatives à la politique monétaire des données

concernant les réserves bancaires et les taux d’intérêt. Ce modèle autorise les relations entre

les variables macroéconomiques dans le système non contraint mais il impose des restrictions

d’identification contemporaines sur l’ensemble des variables relatives au marché de réserves

des banques commerciales. Pour identifier ce modèle les auteurs s’appuient sur les modalités

d’intervention de la banque centrale et évaluent les différents indicateurs de l’orientation de la

politique monétaire qui découlent des modalités d’intervention en testant les restrictions de

suridentification imposées. Dans le cas du Japon Il apparaiterait ainsi intéressant d’employer

le modèle TVP-FAVAR en prenant en compte différentes variables de politique monétaire,

et en évitant ainsi de faire l’hypothèse qu’une variable unique constitue le meilleur indicateur

de la politique monétaire.

164

De plus, l’étude des effets de la politique monétaire nationale dans les différentes

régions a montré que, dans des économies de grande taille, les effets agrégés peuvent cacher

une grande diversité régionale dans la répartition activité/inflation (sur les Etats américains :

Carlino et DeFina, 1998 ; Owyang et Wall, 2003). Il pourrait être fructueux d’estimer des

effets régionaux de la politique monétaire japonaise afin de déterminer dans quelle mesure la

politique d’assouplissement quantitatif s’est transmise de manière différente entre les régions

japonaises, aussi bien pour la production que pour les prix.

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