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

By: Contributor(s): Material type: TextTextSeries: The Econometric and Tinbergen Institutes lecturesPublication details: Princeton : Princeton University Press, ©2016.Description: xix, 275 pages : illustrations ; 23 cmISBN:
  • 9780691161082
Subject(s): DDC classification:
  • 000SB:339.5 23 H538
Contents:
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 -- II Estimation of linearized DSGE models. 4. Metropolis-Hastings algorithms for DSGE models -- 5. Sequential Monte Carlo methods -- 6. Three applications -- 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 -- B. Data sources.
Summary: 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.
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Includes bibliographical references and index.

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 --
II Estimation of linearized DSGE models.
4. Metropolis-Hastings algorithms for DSGE models --
5. Sequential Monte Carlo methods --
6. Three applications --
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 --
B. Data sources.

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.

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