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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Working Paper
Sequential Monte Carlo Sampling for DSGE Models
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.
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