Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/22010
Authors: 
Liesenfeld, Roman
Richard, Jean-François
Year of Publication: 
2006
Series/Report no.: 
Economics working paper / Christian-Albrechts-Universität Kiel, Department of Economics 2006,05
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.
Subjects: 
Autoregressive models
Bayesian posterior analysis
Dynamic latent variables
Gibbs sampling
Metropolis Hastings
Stochastic volatility
Document Type: 
Working Paper

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