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Christian-Albrechts-Universität zu Kiel

Institut für Volkswirtschaftslehre - Department of Economics
Economics Working Papers

Economics Working Papers: Abstract 2006-05




Roman Liesenfeld, Jean-François Richard




Improving MCMC Using Efficient Importance Sampling
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

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