Bayesian Estimation of DSGE Models

Edward P. Herbst; Frank Schorfheide

Published by Princeton University Press 2016

Bayesian Estimation of DSGE Models

Edward P. Herbst and Frank Schorfheide

In the series The Econometric and Tinbergen Institutes Lectures

https://doi.org/10.1515/9781400873739

Overview
Contents

About this book

Dynamic stochastic general equilibrium (DSGE) models have become one of the workhorses of modern macroeconomics and are extensively used for academic research as well as forecasting and policy analysis at central banks. This book introduces readers to state-of-the-art computational techniques used in the Bayesian analysis of DSGE models. The book covers Markov chain Monte Carlo techniques for linearized DSGE models, novel sequential Monte Carlo methods that can be used for parameter inference, and the estimation of nonlinear DSGE models based on particle filter approximations of the likelihood function. The theoretical foundations of the algorithms are discussed in depth, and detailed empirical applications and numerical illustrations are provided. The book also gives invaluable advice on how to tailor these algorithms to specific applications and assess the accuracy and reliability of the computations.

Bayesian Estimation of DSGE Models is essential reading for graduate students, academic researchers, and practitioners at policy institutions.

Author information

Edward P. Herbst is an economist in the Division of Research and Statistics at the Federal Reserve Board. Frank Schorfheide is Professor of Economics at the University of Pennsylvania and research associate at the National Bureau of Economic Research. He also is a fellow of the Penn Institute for Economic Research, a visiting scholar at the Federal Reserve Banks of Philadelphia and New York, and a coeditor of Quantitative Economics. For more, see edherbst.net and sites.sas.upenn.edu/schorf.

Reviews

Well written and well organized, and the topic analyzed is very interesting and current.---Manuel Salvador, MathSciNet

"This timely book collects in one place many of the key Markov chain Monte Carlo methods for numerical Bayesian inference along with many of their recent refinements. Written for applied users, it offers clear descriptions of each algorithm and illustrates how it can be used to estimate dynamic stochastic general equilibrium models in macroeconomics."—James D. Hamilton, Professor of Economics, University of California, San Diego

"This is perhaps the most thorough book available on how to estimate DSGE models using sophisticated Bayesian computation tools. It is an excellent resource for professionals and advanced students of the topic."—Serena Ng, Professor of Economics, Columbia University

"This book depicts valuable and revealing methods for solving, estimating, and analyzing a class of dynamic equilibrium models of the macroeconomy. It describes formally tractable techniques for the study of macroeconomic models that feature transition mechanisms for a large number of underlying shocks. Both authors have played important roles in developing and applying these techniques. This is a terrific resource for how to use these methods in practice."—Lars Peter Hansen, David Rockefeller Distinguished Service Professor of Economics, University of Chicago, and recipient of the Nobel Prize in economics

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Frontmatter Publicly Available Download PDF	i
Contents Publicly Available Download PDF	vii
Figures Requires Authentication Unlicensed Licensed Download PDF	xi
Tables Requires Authentication Unlicensed Licensed Download PDF	xiii
Series Editors’ Introduction Requires Authentication Unlicensed Licensed Download PDF	xv
Preface Requires Authentication Unlicensed Licensed Download PDF	xvii
Part I. Introduction to DSGE Modeling and Bayesian Inference
1. DSGE Modeling Requires Authentication Unlicensed Licensed Download PDF	3
2. Turning a DSGE Model into a Bayesian Model Requires Authentication Unlicensed Licensed Download PDF	14
3. A Crash Course in Bayesian Inference Requires Authentication Unlicensed Licensed Download PDF	29
Part II. Estimation of Linearized DSGE Models
4. Metropolis-Hastings Algorithms for DSGE Models Requires Authentication Unlicensed Licensed Download PDF	65
5. Sequential Monte Carlo Methods Requires Authentication Unlicensed Licensed Download PDF	100
6. Three Applications Requires Authentication Unlicensed Licensed Download PDF	130
Part III. Estimation of Nonlinear DSGE Models
7. From Linear to Nonlinear DSGE Models Requires Authentication Unlicensed Licensed Download PDF	163
8. Particle Filters Requires Authentication Unlicensed Licensed Download PDF	171
9. Combining Particle Filters with MH Samplers Requires Authentication Unlicensed Licensed Download PDF	218
10. Combining Particle Filters with SMC Samplers Requires Authentication Unlicensed Licensed Download PDF	231
Appendix A. Model Descriptions Requires Authentication Unlicensed Licensed Download PDF	241
Appendix B. Data Sources Requires Authentication Unlicensed Licensed Download PDF	249
Bibliography Requires Authentication Unlicensed Licensed Download PDF	257
Index Requires Authentication Unlicensed Licensed Download PDF	271

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