Improving MCMC Using Efficient Importance Sampling

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Economics Working Paper ; No. 2006-05

Classification and Topics

• Classification: Wirtschaft
• Subject: Autoregressive models ; Bayesian posterior analysis ; Dynamic latent variables ; Gibbs sampling ; Metropolis Hastings ; Stochastic volatility ; Monte-Carlo-Methode ; Stochastischer Prozess ; Stichprobenverfahren ; Theorie

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Liesenfeld, Roman ; Richard, Jean-François
• Event: Veröffentlichung
• (who): Kiel University, Department of Economics
• (where): Kiel
• (when): 2006

Further information

• Handle: http://hdl.handle.net/10419/22010
• Last update: 10.03.2025, 11:44 AM CET