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Bayesian Inference on GARCH Models Using the Gibbs Sampler

Luc Bauwens () and Michel Lubrano ()

G.R.E.Q.A.M. from Universite Aix-Marseille III

Abstract: This paper explains how the Gibbs sampler can be used to perform Bayesian inference on GARCH models. Although the Gibbs sampler is usually based on the analytical knowledge of the full conditional posterior densities, such knowledge is not available in regression models with GARCH errors. We show that the Gibbs sampler can be combined with a unidimensional deterministic integration rule applied to each coordinate of the posterior density.

Keywords: TIME SERIES; MODELS; ECONOMETRICS; STATISTICS (search for similar items in EconPapers)
JEL-codes: C10 C11 C15 C20 C22 (search for similar items in EconPapers)
Date: 1996
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Related works:
Journal Article: Bayesian inference on GARCH models using the Gibbs sampler (1998)
Working Paper: Bayesian inference on GARCH models using the Gibbs sampler (1998) Downloads
Working Paper: Bayesian Inference on GARCH Models using the Gibbs Sampler (1996) Downloads
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