Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/22010 
Erscheinungsjahr: 
2006
Schriftenreihe/Nr.: 
Economics Working Paper No. 2006-05
Verlag: 
Kiel University, Department of Economics, Kiel
Zusammenfassung: 
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.
Schlagwörter: 
Autoregressive models
Bayesian posterior analysis
Dynamic latent variables
Gibbs sampling
Metropolis Hastings
Stochastic volatility
Dokumentart: 
Working Paper

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