Arbeitspapier

Improving MCMC Using Efficient Importance Sampling

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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.

Sprache
Englisch

Erschienen in
Series: Economics Working Paper ; No. 2006-05

Klassifikation
Wirtschaft
Thema
Autoregressive models
Bayesian posterior analysis
Dynamic latent variables
Gibbs sampling
Metropolis Hastings
Stochastic volatility
Monte-Carlo-Methode
Stochastischer Prozess
Stichprobenverfahren
Theorie

Ereignis
Geistige Schöpfung
(wer)
Liesenfeld, Roman
Richard, Jean-François
Ereignis
Veröffentlichung
(wer)
Kiel University, Department of Economics
(wo)
Kiel
(wann)
2006

Handle
Letzte Aktualisierung
10.03.2025, 11:44 MEZ

Datenpartner

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Objekttyp

  • Arbeitspapier

Beteiligte

  • Liesenfeld, Roman
  • Richard, Jean-François
  • Kiel University, Department of Economics

Entstanden

  • 2006

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