Sequential Monte Carlo Sampling for DSGE Models
64 Pages Posted: 30 Nov 2012
Date Written: November 7, 2012
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 Analysis, DSGE Models, Monte Carlo Methods, Parallel Computing
JEL Classification: C11, C15, E10
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