Improving MCMC Using Efficient Importance Sampling

Roman Liesenfeld and Jean-Francois Richard

No 2006-05, Economics Working Papers from Christian-Albrechts-University of Kiel, Department of Economics

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 (search for similar items in EconPapers)
Date: 2006
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (1)

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Related works:
Journal Article: Improving MCMC, using efficient importance sampling (2008)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:cauewp:4349

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