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Activity Number: 151
Type: Topic Contributed
Date/Time: Monday, August 3, 2009 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #303521
Title: Particle Learning DSGE Models
Author(s): Francesca Petralia*+ and Carlos M. Carvalho and Hedibert F. Lopes and Hao Chen
Companies: Duke University and The University of Chicago and The University of Chicago and Duke University
Address: Department of Statistical Science, Durham, NC, ,
Keywords:
Abstract:

The use of particle filtering in non-linear and/or non-normal DSGE model has focused on the evaluation of the likelihood function (Ramirez et al (2004)). Under this approach, to perform Bayesian inference on parameters, a combination of Sequential Monte Carlo (SMC) and Markov Chain Monte Carlo algorithms is entertained. In this paper, we use SMC with particle learning to fully estimate non-linear, non-normal DSGE models. The SMC algorithm allows a simultaneous estimation of time-varying state vectors and of fixed parameters. As an example, we have applied our algorithm on a neoclassical growth model, where structural parameters are sequentially estimated compared to currently used MCMC schemes.

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