Sequential Monte Carlo Sampling for DSGE Models

64 Pages Posted: 30 Nov 2012

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: November 7, 2012

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-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

Suggested Citation

Herbst, Edward and Schorfheide, Frank, Sequential Monte Carlo Sampling for DSGE Models (November 7, 2012). FRB of Philadelphia Working Paper No. 12-27. Available at SSRN: https://ssrn.com/abstract=2182710 or http://dx.doi.org/10.2139/ssrn.2182710

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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