Sequential Monte Carlo sampling for DSGE models

Edward Herbst and Frank Schorfheide

No 2013-43, Finance and Economics Discussion Series from Board of Governors of the Federal Reserve System (U.S.)

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 three examples--an artificial state-space model, the Smets and Wouters (2007) model, and Schmitt-Grohe and Uribe's (2012) 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 (2007) model improves its marginal data density and that a slight modification of the prior for the news shock model leads to important 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.

Date: 2013
New Economics Papers: this item is included in nep-dge
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Citations: View citations in EconPapers (4)

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Related works:
Journal Article: SEQUENTIAL MONTE CARLO SAMPLING FOR DSGE MODELS (2014)
Working Paper: Sequential Monte Carlo Sampling for DSGE Models (2013)
Working Paper: Sequential Monte Carlo sampling for DSGE models (2012)
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