Four Essays in Econometrics and Macroeconomics [Elektronische Ressource] / Benjamin Born
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Four Essays in Econometrics and Macroeconomics [Elektronische Ressource] / Benjamin Born

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Four Essays in Econometrics and MacroeconomicsInaugural-Dissertationzur Erlangung des Grades eines Doktorsder Wirtschafts- und Gesellschaftswissenschaftendurch dieRechts- und Staatswissenschaftliche Fakultätder Rheinischen Friedrich-Wilhelms-UniversitätBonnvorgelegt vonBenjamin Bornaus SiegenBonn 2011Dekan: Prof. Dr. Klaus SandmannErstreferent: Prof. Dr. Jörg BreitungZweitreferent: Prof. Dr. Gernot J. MüllerTag der mündlichen Prüfung: 23.09.2011DieseDissertationistaufdemHochschulschriftenserverderULBBonnhttp://hss.ulb.uni-bonn.de/diss_online elektronisch publiziert.AcknowledgmentsMany people have contributed to this thesis with their comments and support.Special thanks go to my main advisor Jörg Breitung, who has been a great supervisor,providing invaluable guidance and steady support during my dissertation. Not onlyhave I greatly benefited from being able to work with him on joint projects, but hehas also always been supportive of my own research interests.The interaction with Gernot Müller kindled my interest in macroeconomic research,and I am thankful for his support throughout my doctoral studies. Especially thethird chapter would not exist in its present form without his constant feedback.I would like to thank my coauthor and office mate of many years, Johannes Pfeifer,for countless discussions ranging from Anticipated Tax Shocks to Zombie Movies. Ihope that our cooperation continues for many years to come.

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Published 01 January 2011
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Four Essays in Econometrics and Macroeconomics
Inaugural-Dissertation
zur Erlangung des Grades eines Doktors
der Wirtschafts- und Gesellschaftswissenschaften
durch die
Rechts- und Staatswissenschaftliche Fakultät
der Rheinischen Friedrich-Wilhelms-Universität
Bonn
vorgelegt von
Benjamin Born
aus Siegen
Bonn 2011Dekan: Prof. Dr. Klaus Sandmann
Erstreferent: Prof. Dr. Jörg Breitung
Zweitreferent: Prof. Dr. Gernot J. Müller
Tag der mündlichen Prüfung: 23.09.2011
DieseDissertationistaufdemHochschulschriftenserverderULBBonnhttp://hss.ulb.uni-
bonn.de/diss_online elektronisch publiziert.Acknowledgments
Many people have contributed to this thesis with their comments and support.
Special thanks go to my main advisor Jörg Breitung, who has been a great supervisor,
providing invaluable guidance and steady support during my dissertation. Not only
have I greatly benefited from being able to work with him on joint projects, but he
has also always been supportive of my own research interests.
The interaction with Gernot Müller kindled my interest in macroeconomic research,
and I am thankful for his support throughout my doctoral studies. Especially the
third chapter would not exist in its present form without his constant feedback.
I would like to thank my coauthor and office mate of many years, Johannes Pfeifer,
for countless discussions ranging from Anticipated Tax Shocks to Zombie Movies. I
hope that our cooperation continues for many years to come.
Large parts of the fourth chapter were written during my stay at the Monetary
Policy Research Division of the European Central Bank. I am very grateful for the
hospitality I experienced during my stay in Frankfurt and I am especially indebted to
my coauthors Michael Ehrmann and Marcel Fratzscher, who gave me the possibility
to collaborate with them.
I am also indebted to Urs Schweizer, Silke Kinzig, and Pamela Mertens for their
tireless efforts in managing the Bonn Graduate School of Economics (BGSE) and in
creating a truly stimulating place for research. In this regard, I would like to thank
the German Research Foundation (DFG) for financial support.
The four years in Bonn would not have been such a great experience without the
colleagues and friends at the Institute of Econometrics, especially Heide Baumung,
iiiJörg Breitung, Norbert Christopeit, Matei Demetrescu, Uli Homm, Christian Pigorsch,
and Jörn Tenhofen; and my fellow grad students Rafael Aigner, Inga van den Bongard,
Deniz Dizdar, Tilman Drerup, Sebastian Ebert, Andreas Esser, Patrick Hürtgen,
Stephan Kurka, Matthias Lang, Juliane Parys, Gregor Schwerhoff, Christian Seel,
Mirko Seithe, Marco Sorge, Christoph Wagner, and Florian Zimmermann. Thank
you, not only for stimulating scientific discussions, but also for great conference trips,
hiking tours, Diablo and Starcraft games, and numerous parties.
Finally, I am deeply indebted to my parents for their patience and continuous
support. Alexandra has accompanied this dissertation since our first joint presentation
in the first-year Macro PhD class, and she has endured all the ups and downs along
the way. Thank you!
ivContents
Introduction 1
1 Simple Regression Based Tests for Spatial Dependence 5
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.2 LM Test Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.3 Regression Variants . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.4 Asymptotic Properties . . . . . . . . . . . . . . . . . . . . . . . . . . 10
1.5 Monte Carlo Simulations . . . . . . . . . . . . . . . . . . . . . . . . . 12
1.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
Appendix to Chapter 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2 Testing for Serial Correlation in Fixed-Effects Panel Data Models 21
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.2 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.3 Test Statistics for Fixed T . . . . . . . . . . . . . . . . . . . . . . . . 24
2.3.1 The Wooldridge-Drucker Test . . . . . . . . . . . . . . . . . . 25
2.3.2 The LM Test . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
2.3.3 A Modified Durbin-Watson Statistic . . . . . . . . . . . . . . 29
2.3.4 Tests for Higher Order Autocorrelation . . . . . . . . . . . . . 30
2.3.5 A Heteroskedasticity Robust Test Statistic . . . . . . . . . . . 33
2.4 Monte Carlo Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
2.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
vContents
Appendix to Chapter 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3 Policy Risk and the Business Cycle 49
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
3.2 Uncertainty: Potential Transmission Channels . . . . . . . . . . . . . 53
3.3 A DSGE-Model with Policy Risk . . . . . . . . . . . . . . . . . . . . 55
3.3.1 Household Sector . . . . . . . . . . . . . . . . . . . . . . . . . 55
3.3.2 Labor Market . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
3.3.3 Firm Side . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
3.3.4 Government Sector . . . . . . . . . . . . . . . . . . . . . . . . 60
3.4 Policy Risk: Time Series Evidence . . . . . . . . . . . . . . . . . . . . 61
3.4.1 Estimation Methodology . . . . . . . . . . . . . . . . . . . . . 62
3.4.2 Estimation Results . . . . . . . . . . . . . . . . . . . . . . . . 64
3.5 Fitting the Model to the Data . . . . . . . . . . . . . . . . . . . . . . 68
3.5.1 Simulated Method of Moments Estimation . . . . . . . . . . . 68
3.5.2 Parameter Estimates . . . . . . . . . . . . . . . . . . . . . . . 69
3.5.3 The Effects of Time-Varying Volatility . . . . . . . . . . . . . 71
3.6 The Aggregate Effects of Policy Risk . . . . . . . . . . . . . . . . . . 73
3.6.1 Impulse Response Analysis . . . . . . . . . . . . . . . . . . . . 73
3.6.2 What Drives the Response to Policy Risk? . . . . . . . . . . . 78
3.6.3 Why Are the Effects of Uncertainty Small? . . . . . . . . . . . 80
3.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
Appendix to Chapter 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
A Data construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
B Econometric Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
C Diagnostics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
4 Central Bank Communication on Financial Stability 109
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
4.2 Motivation and Literature . . . . . . . . . . . . . . . . . . . . . . . . 113
4.3 Measuring Communication and the Effects on Financial Markets . . . 115
4.3.1 Choice of Data Frequency, Data Sample and Relevant Financial
Markets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
viContents
4.3.2 Choice and Identification of Communication Events . . . . . . 117
4.3.3 Measuring the Content of Communications . . . . . . . . . . . 122
4.3.4 The Event Study Methodology . . . . . . . . . . . . . . . . . 124
4.4 The Effects of Financial Stability-Related Communication . . . . . . 128
4.4.1 Stylized Facts About Timing and Content of Communication . 128
4.4.2 Effects of FSRs and Speeches/Interviews . . . . . . . . . . . . 131
4.4.3 Sample Splits and Robustness . . . . . . . . . . . . . . . . . . 134
4.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138
Appendix to Chapter 4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140
Bibliography 151
viiList of Figures
1.1 Size corrected power under homo- and heteroskedasticity (n=241) . . 14
3.1 Time series of exogenous driving processes . . . . . . . . . . . . . . . 63
3.2 Smoothed standard deviations . . . . . . . . . . . . . . . . . . . . . . 67
3.3 Impulse responses to a two-standard deviation uncertainty shock to
capital taxes, labor taxes, and government spending . . . . . . . . . . 74
3.4 Impulse responses to a two-standard deviation uncertainty shock to
monetary policy, TFP, and investment-specific technology . . . . . . . 75
3.5 Impulse responses to a joint two-standard deviation policy risk shock
and to a joint technology risk shock . . . . . . . . . . . . . . . . . . . 76
3.6 Impulse responses to a two-standard deviation uncertainty shock to
capital taxes, labor taxes, and TFP . . . . . . . . . . . . . . . . . . . 79
3.7 Impulse responses to a two-standard deviation policy risk shock –
volatile counterfactual . . . . . . . . . . . . . . . . . . . . . . . . . . 81
k
3.8 Evolution of MCMC sampler over time for τ . . . . . . . . . . . . . 101
n
3.9 Evolution of MCMC over time for τ . . . . . . . . . . . . . 102
3.10 Evolution of MCMC sampler over time for z . . . . . . . . . . . . . . 103
I
3.11 Evolution of MCMC over time for z . . . . . . . . . . . . . 104
3.12 Evolution of MCMC sampler over time for g . . . . . . . . . . . . . . 105
3.13 Evolution of MCMC over time for m . . . . . . . . . . . . . 106
3.14 QQ-plots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
ixList of Figures
4.1 Stock market volatility and the occurrence of speeches and interviews 120
4.2 Evolution of optimism over time . . . . . . . . . . . . . . . . . . . . . 129
4.3 Cumulated abnormal returns after communication events . . . . . . . 132
x