Sequential Monte Carlo Sampling for DSGE Models

42 Pages Posted: 21 Jun 2013

See all articles by Edward Herbst

Edward Herbst

Board of Governors of the Federal Reserve System

Frank Schorfheide

University of Pennsylvania - Department of Economics; Centre for Economic Policy Research (CEPR)

Multiple version iconThere are 3 versions of this paper

Date Written: June 2013

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-Grohé and Uribe's (2012) 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 (2007) 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.

Suggested Citation

Herbst, Edward and Schorfheide, Frank, Sequential Monte Carlo Sampling for DSGE Models (June 2013). NBER Working Paper No. w19152. Available at SSRN: https://ssrn.com/abstract=2282992

Edward Herbst (Contact Author)

Board of Governors of the Federal Reserve System ( email )

20th Street and Constitution Avenue NW
Washington, DC 20551
United States

Frank Schorfheide

University of Pennsylvania - Department of Economics ( email )

Ronald O. Perelman Center for Political Science
133 South 36th Street
Philadelphia, PA 19104-6297
United States

HOME PAGE: http://www.econ.upenn.edu/~schorf

Centre for Economic Policy Research (CEPR)

London
United Kingdom

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