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Bayesian estimation of DSGE models

Author: Edward P Herbst; Frank Schorfheide
Publisher: Princeton : Princeton University Press, [2016] ©2016
Series: Econometric and Tinbergen Institutes lectures.
Edition/Format:   eBook : Document : EnglishView all editions and formats
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,  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Herbst, Edward P., 1984-
Bayesian estimation of DSGE models.
Princeton : Princeton University Press, [2016]
(DLC) 2015023799
(OCoLC)934585068
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Edward P Herbst; Frank Schorfheide
ISBN: 9781400873739 1400873738
OCLC Number: 930489389
Language Note: In English.
Description: 1 online resource (xix, 275 pages).
Contents: Introduction to DSGE modeling and Bayesian inference --
DSGE modeling --
Turning a DSGE model into a Bayesian Model --
A crash course in Bayesian inference --
Estimation of linearized DSGE models --
Metropolis-Hasting algorithms for DSGE models --
Sequential Monte Carlo methods --
Three applications --
Estimation of nonlinear DSGE models --
From linear to nonlinear DSGE models --
Particle filters --
Combining particle filters with MH samplers --
Combining particle filters with SMC samplers.
Series Title: Econometric and Tinbergen Institutes lectures.
Responsibility: Edward P. Herbst and Frank Schorfheide.
More information:

Abstract:

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.--

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

 
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