Université libre de Bruxelles
Solvay Brussels School of Economics and Management
European Center for Advanced Research in Economics and Statistics
Essays on Monetary policy, Low Inflation and the Business Cycle
PhD candidate:
Antonio M. Conti
Supervisor:
Professor Robert Kollmann
Thesis Committee:
Prof. Marjorie Gassner, Université libre de Bruxelles Prof. Robert Kollmann, Université libre de Bruxelles
Prof. Raf Wouters, National Bank of Belgium and Université libre de Bruxelles Prof. Ferre De Graeve, Katholieke Universiteit Leuven
Prof. Geert Peersman, Ghent University
Prof. Glenn Magerman, Université libre de Bruxelles
A dissertation submitted to the Université libre de Bruxelles in partial fulfillment of the requirements for the degree
of
Doctor of Philosophy in Quantitative Economics
November 2017
Contents
General Overview 13
1 Do Euro Area Countries Respond Asymmetrically to the Common Mon-
etary Policy? 15
1.1 Introduction . . . 15
1.2 Structural Dynamic Factor model . . . 17
1.2.1 Structural Dynamic Factor models in the Euro Area . . . 19
1.2.2 Testing for asymmetries . . . 20
1.3 Model setup . . . 22
1.3.1 Data and data treatment . . . 22
1.3.2 Number of common shocks and factors . . . 23
1.4 Identification of the monetary policy shock . . . 25
1.5 Results . . . 26
1.5.1 Cross-country differences before 1999 . . . 28
1.5.2 Cross-country differences after 1999 . . . 29
1.6 Discussion and conclusions . . . 34
2 Low Inflation and Monetary policy in the Euro Area 35 2.1 Introduction . . . 35
2.2 The Bayesian VAR . . . 40
2.2.1 The model . . . 40
2.2.2 The data . . . 41
2.2.3 The identification of the shocks . . . 41
2.2.4 Inference . . . 44
2.3 Impulse responses . . . 44
2.4 The drivers of inflation and economic activity . . . 47 3
4 CONTENTS
2.4.1 The drivers of inflation . . . 47
2.4.2 The drivers of economic activity . . . 51
2.4.3 The role of monetary policy . . . 53
2.5 Country-level results . . . 57
2.6 Robustness . . . 61
2.6.1 Structural changes in the transmission of shocks . . . 62
2.6.2 The role of credit supply shocks . . . 63
2.6.3 Accounting for sovereign spreads . . . 64
2.6.4 Additional insights on the role of oil shocks . . . 66
2.7 Concluding remarks . . . 67
3 The Financial Stability Dark Side of Monetary Policy 69 3.1 Introduction . . . 69
3.2 The Dark Side Argument . . . 72
3.3 Data . . . 74
3.4 Predictive regressions . . . 75
3.5 A non–linear model of the monetary transmission mechanism . . . 77
3.6 Bond markets and monetary policy shocks . . . 80
3.7 Discussion . . . 84
3.7.1 Accounting for uncertainty and consumer confidence . . . 84
3.7.2 Recessions, broader financial conditions indicators, alternative EBP transformations . . . 85
3.7.3 Credit shocks . . . 87
3.8 Bond markets and macroeconomic news . . . 90
3.9 An alternative model: Sign–dependent local projections . . . 93
3.10 Conclusions . . . 97
4 Has the FED Fallen Behind the Curve? Evidence from VAR Models 99 4.1 Introduction . . . 99
4.2 Bayesian VAR framework . . . 100
4.2.1 Model . . . 100
4.2.2 Data and Identification of the Structural Shocks . . . 101
4.2.3 Findings of the Structural analysis . . . 102
4.3 Conditional forecasts and scenario analysis . . . 104
4.3.1 Findings of the counterfactuals . . . 104
CONTENTS 5
4.4 Conclusions . . . 105
A Supplementary data and findings 123 A-1 Supplementary Material to Chapter 1 . . . 123
The Euro Area dataset . . . 124
Determining the Number Factors . . . 128
A-2 Supplementary material to Chapter 3 . . . 129
Supplementary Figures . . . 129
A-3 Supplementary material to Chapter 4 . . . 133
Impulse responses . . . 133
Specification, transformations and sources . . . 134
Inference and prior settings . . . 135
Identification . . . 136
SVAR . . . 138
Conditional forecast . . . 138
Updating data to 2017:Q1 . . . 140
6 CONTENTS
List of Figures
1.1 CUSUM Square Test on the Static Factors . . . 21 1.2 Impulse Responses to a Monetary Policy Shock: Euro Area Ag-
gregates . . . 27 1.3 Impulse Responses to a Monetary Policy Shock: CPI and GDP . 29 1.4 Impulse Responses to a Monetary Policy ShockConsumption . . . 30 1.5 Impulse Responses to a Monetary Policy ShockInvestment . . . . 30 1.6 Impulse Responses to a Monetary Policy ShockUnemployment rate 31 1.7 Cross–country Asymmetries: CPI and GDP . . . 32 1.8 Cross–country Asymmetries: Consumption, Investment and Unem-
ployment rate . . . 33 2.1 Inflation, inflation expectations and monetary policy in the
euro area . . . 37 2.2 Impulse responses to the five shocks in the baseline specification 46 2.3 Historical decomposition of euro area HICP inflation . . . 49 2.4 Historical decomposition of euro area core inflation . . . 50 2.5 Historical decomposition of euro area real GDP growth . . . 52 2.6 Historical decomposition of euro area real interest rate . . 54 2.7 Historical decomposition of euro area HICP inflation and real
GDP growth: the role of unconventional monetary policy . . 56 2.8 Historical decomposition of euro area inflation and real GDP:
euro area countries . . . 59 2.9 Historical decomposition of euro area inflation and real GDP:
euro area countries (continued) . . . 60 2.10 Heterogeneity in contribution of monetary policy: EONIA vs.
shadow rate . . . 61 7
8 LIST OF FIGURES
2.11 Impulse responses to an adverse credit supply shock . . . 64
2.12 Historical decomposition of euro area inflation and real GDP growth: identifying credit supply shocks . . . 65
3.1 GZ spread, Moody’s BAA-AAA spread and Excess Bond Premium 75 3.2 The impact of monetary shocks on economic activity . . . 82
3.3 The impact of monetary shocks on EBP . . . 83
3.4 The impact of monetary shocks on economic activity: Account- ing for uncertainty and confidence . . . 86
3.5 EBP and its transformations capturing asymmetries . . . 88
3.6 The impact of an EBP (credit spread) shock on economic ac- tivity . . . 89
3.7 Impulse response to a monetary tightening: Ramey (2016) method 94 3.8 Impulse response: tightening vs. easing, Ramey (2016) method 96 3.9 Impulse response: tightening vs. easing, Ramey (2016) method, sample up to 2007M12 . . . 97
4.1 FED monetary policy stance. . . 101
4.2 Historical decomposition . . . 103
4.3 Forecasts . . . 105
4.4 Scenario: FFR dynamics and the business cycle . . . 106
A-1 The impact of monetary shocks on economic activity, condi- tioning on recessions (Specification 2 on table 3.2 of the paper)129 A-2 The impact of monetary shocks on economic activity: Chicago FCI as financial conditions indicator (Specification 3 in table 3.2 of the paper) . . . 130
A-3 The impact of monetary shocks on economic activity, Sdif f as non–linear transformation of EBP (Specification 4 in table 3.2 of the paper) . . . 131
A-4 The impact of monetary shocks on economic activity, Splus as non–linear transformation of EBP (Specification 5 in table 3.2 of the paper) . . . 132
A-5 IRF to monetary policy shock . . . 133
A-6 IRF to the other 4 identified structural shocks . . . 134
A-7 World imports and US exports . . . 136
LIST OF FIGURES 9 A-8 Scenario: FFR, PCE core inflation dynamics and business cycle139 A-9 Historical decomposition: BVAR with nominal wages and sam-
ple updated to 2017:Q1 . . . 141
10 LIST OF FIGURES
List of Tables
1.1 Testing for Structural Break in the Factor Loadings . . . 20
1.2 The Distribution of Autocorrelations Light vs. Heavy . . . 23
1.3 Determining the Number of Common Shocks: Onatski Test . . . . 24
1.4 Cumulated Explained Variance . . . 24
1.5 Comovements in the Euro Area Explained Variance . . . 25
2.1 Sign restrictions used for identification . . . 42
3.1 Credit spreads and economic activity: non–linear GZ regres- sions . . . 78
3.2 Multivariate structural model: list of alternative specifica- tions . . . 85
3.3 Predictive power of unemployment news for economic activity 91 3.4 Reaction of EBP to bad and good unemployment news . . . 92
3.5 Unemployment and monetary policy news . . . 93
4.1 Sign restrictions . . . 102
4.2 Forecasts comparison . . . 106
A.1 Determining the Number of Static Factors . . . 128
A.2 Determining the Number of Common Shocks . . . 128
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