Sequential Monte Carlo Sampling for DSGE Models

Edward Herbst and Frank Schorfheide

No 19152, NBER Working Papers from National Bureau of Economic Research, Inc

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.

JEL-codes: C11 C15 E10 (search for similar items in EconPapers)
Date: 2013-06
New Economics Papers: this item is included in nep-cmp and nep-dge
Note: EFG ME
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (18)

Published as Edward Herbst & Frank Schorfheide, 2014. "Sequential Monte Carlo Sampling For Dsge Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(7), pages 1073-1098, November.

Downloads: (external link)
http://www.nber.org/papers/w19152.pdf (application/pdf)

Related works:
Journal Article: SEQUENTIAL MONTE CARLO SAMPLING FOR DSGE MODELS (2014)
Working Paper: Sequential Monte Carlo sampling for DSGE models (2013)
Working Paper: Sequential Monte Carlo sampling for DSGE models (2012)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nbr:nberwo:19152

Ordering information: This working paper can be ordered from
http://www.nber.org/papers/w19152

Access Statistics for this paper

More papers in NBER Working Papers from National Bureau of Economic Research, Inc National Bureau of Economic Research, 1050 Massachusetts Avenue Cambridge, MA 02138, U.S.A.. Contact information at EDIRC.
Bibliographic data for series maintained by ().