SEQUENTIAL MONTE CARLO SAMPLING FOR DSGE MODELS

Edward Herbst and Frank Schorfheide

Journal of Applied Econometrics, 2014, vol. 29, issue 7, 1073-1098

Abstract: We develop a sequential Monte Carlo (SMC) algorithm for estimating Bayesian dynamic stochastic general equilibrium (DSGE) models; wherein a particle approximation to the posterior is built iteratively through tempering the likelihood. Using two empirical illustrations consisting of the Smets and Wouters model and a larger news shock model we show that the SMC algorithm is better suited for multimodal and irregular posterior distributions than the widely used random walk Metropolis–Hastings algorithm. We find that a more diffuse prior for the Smets and Wouters model improves its marginal data density and that a slight modification of the prior for the news shock model leads to drastic changes in the posterior inference about the importance of news shocks for fluctuations in hours worked. Unlike standard Markov chain Monte Carlo (MCMC) techniques; the SMC algorithm is well suited for parallel computing. Copyright © 2014 John Wiley & Sons, Ltd.

Date: 2014
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Citations: View citations in EconPapers (88)

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https://doi.org/10.1002/jae.2397

Related works:
Working Paper: Sequential Monte Carlo sampling for DSGE models (2013)
Working Paper: Sequential Monte Carlo Sampling for DSGE Models (2013)
Working Paper: Sequential Monte Carlo sampling for DSGE models (2012)
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