Working Paper

Sequential Monte Carlo Sampling for DSGE Models

Edward Herbst &
Frank Schorfheide

November 2012

WP 12-27 — The authors 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 artifcial state-space model, the Smets and Wouters (2007) model, and Schmitt-Grohé and Uribe’s (2012) news shock model the authors 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.

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