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
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)
References: Add references at CitEc
Citations: View citations in EconPapers (16) Track citations by RSS feed
There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.
Journal Article: Bayesian inference on GARCH models using the Gibbs sampler (1998)
Working Paper: Bayesian inference on GARCH models using the Gibbs sampler (1998)
Working Paper: Bayesian Inference on GARCH Models using the Gibbs Sampler (1996)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:fth:aixmeq:96a21
Access Statistics for this paper
More papers in G.R.E.Q.A.M. from Universite Aix-Marseille III G.R.E.Q.A.M., (GROUPE DE RECHERCHE EN ECONOMIE QUANTITATIVE D'AIX MARSEILLE), CENTRE DE VIEILLE CHARITE, 2 RUE DE LA CHARITE, 13002 MARSEILLE.. Contact information at EDIRC.
Bibliographic data for series maintained by Thomas Krichel ().