Bayesian estimation of DSGE models / Frank Schorfheide, Edward P. Herbst.

Format:

Book

Author/Creator:

Herbst, Edward P., author.

Schorfheide, Frank, author.

Series:

Econometric and Tinbergen Institutes lectures.

The Econometric and Tinbergen Institutes Lectures

Language:

English

Subjects (All):

Equilibrium (Economics)--Mathematical models.

Equilibrium (Economics).

Bayesian statistical decision theory.

Stochastic analysis.

Econometrics.

Physical Description:

1 online resource (296 p.)

Place of Publication:

Princeton, NJ : Princeton University Press, [2015]

Language Note:

English

Summary:

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.

Contents:

Frontmatter

Contents

Figures

Tables

Series Editors' Introduction

Preface

Part I. Introduction to DSGE Modeling and Bayesian Inference

1. DSGE Modeling

2. Turning a DSGE Model into a Bayesian Model

3. A Crash Course in Bayesian Inference

Part II. Estimation of Linearized DSGE Models

4. Metropolis-Hastings Algorithms for DSGE Models

5. Sequential Monte Carlo Methods

6. Three Applications

Part III. Estimation of Nonlinear DSGE Models

7. From Linear to Nonlinear DSGE Models

8. Particle Filters

9. Combining Particle Filters with MH Samplers

10. Combining Particle Filters with SMC Samplers

Appendix A. Model Descriptions

Appendix B. Data Sources

Bibliography

Index

Notes:

Description based upon print version of record.

Includes bibliographical references and index.

Description based on online resource; title from PDF title page (publisher's Web site, viewed 08. Jul 2019)

ISBN:

9781400873739

1400873738

OCLC:

930489389