SEQUENTIAL MONTE CARLO SAMPLING FOR DSGE MODELS

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 two empirical illustrations consisting of the Smets and Wou...

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Bibliographic Details
Published in:Journal of applied econometrics (Chichester, England) England), 2014-11, Vol.29 (7), p.1073-1098
Main Authors: Herbst, Edward, Schorfheide, Frank
Format: Article
Language:English
Subjects:
Algorithms
Bayesian analysis
Bayesian method
Economic models
Economic shock
General economic equilibrium
Markovian processes
Monte Carlo simulation
Random walk theory
Stochastic processes
Studies
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Summary: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 two empirical illustrations consisting of the Smets and Wouters model and a larger 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 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.
ISSN:0883-7252
1099-1255
DOI:10.1002/jae.2397