Sequential Monte Carlo sampling for DSGE models
AbstractWe 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.
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Bibliographic InfoPaper provided by Federal Reserve Bank of Philadelphia in its series Working Papers with number 12-27.
Date of creation: 2012
Date of revision:
Other versions of this item:
- Edward P. Herbst & Frank Schorfheide, 2013. "Sequential Monte Carlo Sampling for DSGE Models," NBER Working Papers 19152, National Bureau of Economic Research, Inc.
- Edward P. Herbst & Frank Schorfheide, 2013. "Sequential Monte Carlo sampling for DSGE models," Finance and Economics Discussion Series 2013-43, Board of Governors of the Federal Reserve System (U.S.).
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- E10 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - General
This paper has been announced in the following NEP Reports:
- NEP-ALL-2012-12-15 (All new papers)
- NEP-CMP-2012-12-15 (Computational Economics)
- NEP-DGE-2012-12-15 (Dynamic General Equilibrium)
- NEP-ECM-2012-12-15 (Econometrics)
- NEP-ORE-2012-12-15 (Operations Research)
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