Essays in Econometrics and Macroeconomics [Elektronische Ressource] / Jörn Tenhofen. Rechts- und Staatswissenschaftliche Fakultät

Jörn Tenhofen

Informations

• Publié par: rheinische_friedrich-wilhelms-universitat_bonn
• Publié le: 01 janvier 2011
• Nombre de lectures: 17
• Langue: English
• Poids de l'ouvrage: 1 Mo

Extrait

Essays in Econometrics and Macroeconomics
Inaugural-Dissertation
zur Erlangung des Grades eines Doktors
der Wirtschafts- und Gesellschaftswissenschaften
durch die
Rechts- und Staatswissenschaftliche Fakultat¨
der Rheinischen Friedrich-Wilhelms-Universita¨t
Bonn
vorgelegt von
Jorn Tenhofen¨
aus Rhede (Westf.)
Bonn 2011ii
Dekan: Prof. Dr. Klaus Sandmann
Erstreferent: Prof. Dr. Jorg Breitung¨
Zweitreferent: Prof. Monika Merz, Ph.D.
Tag der mundlichen Prufung: 15. Februar 2011¨ ¨
Diese Dissertation ist auf dem Hochschulschriftenserver der ULB Bonn
(http://hss.ulb.uni-bonn.de/diss online) elektronisch publiziert.iii
to my parentsivAcknowledgments
This dissertation would not have been possible without the help and support of many
people. I owe all of them a large debt of gratitude.
First and foremost, I am deeply grateful to my two main advisors, J¨org Breitung
and Monika Merz, for their continuing support, guidance, and advice throughout
the dissertation process. I would especially like to thank them for tolerating my
“disparate” research interests. Jorg Breitung gave me the freedom and support to¨
develop and follow my own research agenda. He was always ready to discuss my work
and other related issues and gave invaluable advice whenever I needed it. I learned
a lot from him about doing research – in econometrics in particular –, creative work,
and beyond. Monika Merz made countless suggestions and comments to improve my
work at all stages of the dissertation project. She forced me to really think all issues
through and alerted me to the important role played by the presentation of a research
idea. I am particularly indebted to her for her tireless efforts to make my unique and
extremely stimulating research visit at Columbia University possible.
I would also like to thank my co-authors, Jorg Breitung and Guntram Wolff, for¨
theirexcellentcooperationandchallengingaswellasstimulatingdiscussions. Ilearned
a lot during our collaboration, about the respective topics and conducting research
more generally.
During the years, the Institute of Econometrics has almost become a second home
for me. Its pleasant and stimulating atmosphere provided the perfect basis for my
work, as well as a starting point for some recreational activities. Many thanks to all
present and former members of the Institute, especially Heide Baumung, Benjamin
Born, Jo¨rg Breitung, Norbert Christopeit, Matei Demetrescu, Uli Homm, Michael
Massmann, as well as Christian and Uta Pigorsch, who made this possible!
Parts of the dissertation were written while I was a visiting scholar at Columbia
University during the 2008-9 academic year. This was a fascinating and extremely
stimulating experience and I would like to thank the entire Department of Economics
vvi
at Columbia University for their help, support, and hospitality during my visit. In
particular, I am grateful to Mike Woodford, who was so kind to sponsor my stay. I
benefited a lot from his support, the open discussions with him, and his numerous
helpful and constructive comments on my work. Many thanks also go to Jushan Bai,
John Leahy, Stephanie Schmitt-Groh´e, and Mart´ın Uribe for insightful conversations
and so many important suggestions related to my research. Financial support from
the Heinrich Hertz foundation of the state of North Rhine-Westphalia and from the
German Academic Exchange Service (DAAD) is gratefully acknowledged.
I am also indebted to Urs Schweizer, Ju¨rgen von Hagen, as well as Silke Kinzig
for the tireless efforts in managing the Bonn Graduate School of Economics (BGSE)
andtheresearchtraininggroup“QuantitativeEconomics”(Graduiertenkolleg). These
institutions provided an invaluable platform for my doctoral studies and my research
in general. In this regard, I would like to thank the German Research Foundation
(DFG) for financial support.
Many thanks also go to my fellow graduate students as well as various members of
the Department of Economics at the University of Bonn, in particular Almut Balleer,
Zeno Enders, Michael Evers, Ju¨rgen Gaul, Stefan Koch, Matthias Lux, Julian Mer-
schen, Daniel Mu¨ller, Gernot Mu¨ller, Johannes Pfeifer, Thomas Rieck, and Philipp
Wichardt, for many insightful discussions and a lot of fun.
Finally, and most importantly, I owe my family, my parents in particular, an
immeasurabledebtofgratitude. Withouttheirunconditionalandcontinuingsupport,
I would not have gone so far. Steffi’s patience, encouragement, and enduring belief in
me deserves more than just gratitude.Contents
Introduction 1
1 GLS estimation of dynamic factor models 13
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
1.2 The dynamic factor model . . . . . . . . . . . . . . . . . . . . . . . . . 17
1.3 The PC-GLS estimator . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
1.4 Asymptotic distribution of the two-step PC-GLS estimator . . . . . . . 24
1.5 Asymptotic efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
1.6 Small sample properties . . . . . . . . . . . . . . . . . . . . . . . . . . 32
1.6.1 Simulation in a controlled environment . . . . . . . . . . . . . . 33
1.6.1.1 Autocorrelation and heteroskedasticity . . . . . . . . . 34
1.6.1.2 Introducing cross-sectional correlation . . . . . . . . . 38
1.6.1.3 The hybrid estimator . . . . . . . . . . . . . . . . . . . 40
1.6.2 Simulation based on Stock and Watson’s (2005) data set . . . . 43
1.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
Appendix to Chapter 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
2 Optimal monetary policy under labor market frictions: the role of
wage rigidity and markup shocks 59
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
2.2 A simple New-Keynesian model with labor market frictions . . . . . . . 64
2.2.1 Economic environment . . . . . . . . . . . . . . . . . . . . . . . 64
2.2.2 Allocating resources . . . . . . . . . . . . . . . . . . . . . . . . 67
2.2.3 Heterogeneous wage setting . . . . . . . . . . . . . . . . . . . . 69
2.3 Introducing markup shocks . . . . . . . . . . . . . . . . . . . . . . . . . 75
viiviii
2.3.1 Equilibrium in the decentralized economy with flexible prices . . 76
2.3.2 Equilibrium in the decentralized economy with sticky prices . . 79
2.4 Monetary policy analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 84
2.4.1 Two polar cases and optimal monetary policy . . . . . . . . . . 85
2.4.2 Calibration and dynamics of the economy . . . . . . . . . . . . 87
2.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
3 Does anticipation of government spending matter? The role of
(non-)defense spending 99
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
3.2 An expectation augmented VAR . . . . . . . . . . . . . . . . . . . . . . 103
3.2.1 A simplified setting: the general idea of the approach . . . . . . 106
3.2.2 The general setting: estimating an expectation augmented VAR 110
3.3 Application to simulated data . . . . . . . . . . . . . . . . . . . . . . . 113
3.4 Empirical investigation . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
3.4.1 Data and elasticities . . . . . . . . . . . . . . . . . . . . . . . . 121
3.4.2 Total government expenditure . . . . . . . . . . . . . . . . . . . 122
3.4.3 Defense expenditure . . . . . . . . . . . . . . . . . . . . . . . . 125
3.4.4 Non-defense expenditure . . . . . . . . . . . . . . . . . . . . . . 128
3.5 Robustness checks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132
3.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137
Appendix to Chapter 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
Concluding remarks 147
Bibliography 152List of Figures
1.1 Histogram of the sample variances . . . . . . . . . . . . . . . . . . . . . 16
1.2 Histogram of the sample autocorrelations . . . . . . . . . . . . . . . . . 16
1.3 Scree plots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
2.1 Unemployment stabilization regime . . . . . . . . . . . . . . . . . . . . 90
2.2 Inflation stabilization regime . . . . . . . . . . . . . . . . . . . . . . . . 91
2.3 Optimal policy regime . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
2.4 Efficient policy frontier . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
3.1 Sequence of events . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
3.2 Theoretical and VAR impulse responses . . . . . . . . . . . . . . . . . . 120
3.3 Standard VAR: government expenditure . . . . . . . . . . . . . . . . . 123
3.4 Expectation augmented VAR: government expenditure . . . . . . . . . 124
3.5 Real per capita government spending . . . . . . . . . . . . . . . . . . . 126
3.6 Ratio of defense expenditure to GDP (in percent) . . . . . . . . . . . . 126
3.7 Standard VAR: defense expenditure . . . . . . . . . . . . . . . . . . . . 127
3.8 Expectation augmented VAR: defense expenditure . . . . . . . . . . . . 127
3.9 Standard VAR: non-defense expenditure . . . . . . . . . . . . . . . . . 129
3.10 Expectation augmented VAR: non-defense expenditure . . . . . . . . . 129
3.11 Standard V