Sequential Monte Carlo sampling for DSGE models

Edward Herbst and Frank Schorfheide

No 12-27, Working Papers from Federal Reserve Bank of Philadelphia

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-Grohe and Uribe's (2012) news shock model we show that the SMC algorithm is better suited for multi-modal and irregular posterior distributions than the widely-used random walk Metropolis-Hastings algorithm. Unlike standard Markov chain Monte Carlo (MCMC) techniques, the SMC algorithm is well suited for parallel computing.

Keywords: Bayesian; statistical; decision; theory (search for similar items in EconPapers)
Date: 2012
New Economics Papers: this item is included in nep-cmp, nep-dge, nep-ecm and nep-ore
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Citations: View citations in EconPapers (60)

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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 (2013)
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