Methodologies for analyzing the forces that move and shape national economies have advanced markedly in the last thirty years, enabling economists as never before to unite theoretical and empirical research and align measurement with theory. In Structural Macroeconometrics, David DeJong and Chetan Dave provide the unified overview and in-depth treatment analysts need to apply these latest theoretical models and empirical techniques.
The authors' emphasis throughout is on time series econometrics. DeJong and Dave detail methods available for solving dynamic structural models and casting solutions in the form of statistical models with empirical implications that may be analyzed either analytically or numerically. They present the full range of methodologies for characterizing and evaluating these empirical implications, including calibration exercises, method-of-moment procedures, and likelihood-based procedures, both classical and Bayesian. The book is complete with a rich array of implementation algorithms, sample empirical applications, and supporting computer code.
Structural Macroeconometrics is tailored specifically to equip readers with a set of practical tools that can be used to expedite their entry into the field. DeJong and Dave's uniquely accessible, how-to approach makes this the ideal textbook for graduate students seeking an introduction to macroeconomics and econometrics and for advanced students pursuing applied research in macroeconomics. The book's historical perspective, along with its broad presentation of alternative methodologies, makes it an indispensable resource for academics and professionals.
Author(s): David N. DeJong and Chetan Dave
Publisher: Princeton University Press
Year: 2007
Language: English
Pages: 342
List of Figures ix
List of Tables xi
Preface xiii
PART I: MODEL AND DATA PREPARATION
Chapter 1: Introduction 3
Chapter 2: Approximating and Solving DSGE Models 11
Chapter 3: Removing Trends and Isolating Cycles 31
Chapter 4: Summarizing Time Series Behavior 55
Chapter 5: DSGE Models: Three Examples 87
PART II: EMPIRICAL METHODS
Chapter 6: Calibration 119
Chapter 7: Matching Moments 151
Chapter 8: Maximum Likelihood 180
Chapter 9: Bayesian Methods 219
PART III: BEYOND LINEARIZATION
Chapter 10: Nonlinear Approximation Methods 267
Chapter 11: Implementing Nonlinear Approximations Empirically 290
Bibliography 315
Index 327