Sequential Monte Carlo Sampling for DSGE Models / Edward P. Herbst, Frank Schorfheide.

Format:

Book

Author/Creator:

Herbst, Edward P.

Contributor:

National Bureau of Economic Research.

Schorfheide, Frank.

Series:

Working Paper Series (National Bureau of Economic Research) no. w19152.

NBER working paper series no. w19152

Language:

English

Physical Description:

1 online resource: illustrations (black and white);

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 2013.

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 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.

Notes:

Print version record

June 2013.