Improving MCMC Using Efficient Importance Sampling

34 Pages Posted: 19 May 2006

See all articles by Roman Liesenfeld

Roman Liesenfeld

University of Cologne, Department of Economics

Jean-Francois Richard

University of Pittsburgh - Department of Economics

Date Written: May 15, 2006

Abstract

This paper develops a systematic Markov Chain Monte Carlo (MCMC) framework based upon Efficient Importance Sampling (EIS) which can be used for the analysis of a wide range of econometric models involving integrals without an analytical solution. EIS is a simple, generic and yet accurate Monte-Carlo integration procedure based on sampling densities which are chosen to be global approximations to the integrand. By embedding EIS within MCMC procedures based on Metropolis-Hastings (MH) one can significantly improve their numerical properties, essentially by providing a fully automated selection of critical MCMC components such as auxiliary sampling densities, normalizing constants and starting values. The potential of this integrated MCMC-EIS approach is illustrated with simple univariate integration problems and with the Bayesian posterior analysis of stochastic volatility models and stationary autoregressive processes.

Keywords: Autoregressive models, Bayesian posterior analysis, Dynamic latent variables, Gibbs sampling, Metropolis Hastings, Stochastic volatility

JEL Classification: C1, C15, C22

Suggested Citation

Liesenfeld, Roman and Richard, Jean-Francois, Improving MCMC Using Efficient Importance Sampling (May 15, 2006). Available at SSRN: https://ssrn.com/abstract=903136 or http://dx.doi.org/10.2139/ssrn.903136

Roman Liesenfeld (Contact Author)

University of Cologne, Department of Economics ( email )

Albertus-Magnus-Platz
D-50931 Köln
Germany

Jean-Francois Richard

University of Pittsburgh - Department of Economics ( email )

4901 Wesley Posvar Hall
230 South Bouquet Street
Pittsburgh, PA 15260
United States
412-648-1750 (Phone)

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
190
Abstract Views
1,169
rank
178,080
PlumX Metrics