Resource title

Improving MCMC Using Efficient Importance Sampling

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Resource description

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.

Resource author

Roman Liesenfeld, Jean-Fran├žois Richard

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Resource language

eng

Resource content type

text/html

Resource resource URL

http://hdl.handle.net/10419/22010

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Adapt according to the presented license agreement and reference the original author.