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Sequential Monte Carlo Sampling for DSGE Models

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  • Edward P. Herbst
  • Frank Schorfheide

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 consisting of an artificial state-space model, the Smets and Wouters (2007) model, and Schmitt-Grohé 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 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.

Suggested Citation

  • Edward P. Herbst & Frank Schorfheide, 2013. "Sequential Monte Carlo Sampling for DSGE Models," NBER Working Papers 19152, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:19152
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    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • E10 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - General

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