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Bayesian Estimation of DSGE Models$
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Edward P. Herbst and Frank Schorfheide

Print publication date: 2015

Print ISBN-13: 9780691161082

Published to Princeton Scholarship Online: October 2017

DOI: 10.23943/princeton/9780691161082.001.0001

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Metropolis-Hastings Algorithms for DSGE Models

Metropolis-Hastings Algorithms for DSGE Models

Chapter:
(p.65) Chapter 4 Metropolis-Hastings Algorithms for DSGE Models
Source:
Bayesian Estimation of DSGE Models
Author(s):

Edward P. Herbst

Frank Schorfheide

Publisher:
Princeton University Press
DOI:10.23943/princeton/9780691161082.003.0004

This chapter talks about the most widely used method to generate draws from posterior distributions of a DSGE model: the random walk MH (RWMH) algorithm. The DSGE model likelihood function in combination with the prior distribution leads to a posterior distribution that has a fairly regular elliptical shape. In turn, the draws from a simple RWMH algorithm can be used to obtain an accurate numerical approximation of posterior moments. However, in many other applications, particularly those involving medium- and large-scale DSGE models, the posterior distributions could be very non-elliptical. Irregularly shaped posterior distributions are often caused by identification problems or misspecification. In lieu of the difficulties caused by irregularly shaped posterior surfaces, the chapter reviews various alternative MH samplers, which use alternative proposal distributions.

Keywords:   RWMH algorithm, DSGE model, prior distribution, posterior distribution, MH samplers, random walk MH

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