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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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Sequential Monte Carlo Methods

Sequential Monte Carlo Methods

(p.100) Chapter 5 Sequential Monte Carlo Methods
Bayesian Estimation of DSGE Models

Edward P. Herbst

Frank Schorfheide

Princeton University Press

This chapter analyzes Sequential Monte Carlo (SMC) algorithms and how they were initially developed to solve filtering problems that arise in nonlinear state–space models. The first paper that applied SMC techniques to posterior inference in DSGE models is Creal (2007). Herbst and Schorfheide (2014) developed the algorithm further, provided some convergence results for an adaptive version of the algorithm, and showed that a properly tailored SMC algorithm delivers more reliable posterior inference for largescale DSGE models with multimodal posteriors than the widely used RMWHV algorithm. An additional advantage of the SMC algorithms over MCMC algorithms, on the computational front, highlighted by Durham and Geweke (2014), is that SMC is much more amenable to parallelization.

Keywords:   SMC algorithms, state–space models, posterior inference, DSGE models, multimodal posteriors, RMWHV algorithm, MCMC algorithms

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