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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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Combining Particle Filters with SMC Samplers

Combining Particle Filters with SMC Samplers

Chapter:
(p.231) Chapter 10 Combining Particle Filters with SMC Samplers
Source:
Bayesian Estimation of DSGE Models
Author(s):

Edward P. Herbst

Frank Schorfheide

Publisher:
Princeton University Press
DOI:10.23943/princeton/9780691161082.003.0010

This chapter combines the SMC algorithm with the particle filter approximation of the likelihood function to develop an SMC2 algorithm. As with the PFMH algorithm, the goal is to obtain a posterior sampler for the DSGE model parameters for settings in which the likelihood function of the DSGE model cannot be evaluated with the Kalman filter. The starting point is the SMC Algorithm 8. The chapter adds data sequentially to the likelihood function rather than tempering the entire likelihood function. Moreover, the evaluation of the incremental and the full likelihood function in the correction and mutation steps of Algorithm 8 are replaced by the evaluation of the respective particle filter approximations.

Keywords:   SMC algorithm, particle filter approximation, posterior sampler, Kalman filter, DSGE model, likelihood function

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