Bayesian Inference on GARCH Models Using the Gibbs Sampler.

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.

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Year of publication:	1996
Authors:	Bauwens, L. ; Lubrano, M.
Institutions:	Groupement de Recherche en Économie Quantitative d'Aix-Marseille (GREQAM), Aix-Marseille School of Economics (AMSE)
Subject:	TIME SERIES \| MODELS \| ECONOMETRICS \| STATISTICS

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Series:	G.R.E.Q.A.M..
Type of publication:	Book / Working Paper
Notes:	26 pages
Classification:	C10 - Econometric and Statistical Methods: General. General ; C11 - Bayesian Analysis ; C15 - Statistical Simulation Methods; Monte Carlo Methods ; C20 - Econometric Methods: Single Equation Models. General ; C22 - Time-Series Models
Source:	RePEc - Research Papers in Economics

Persistent link: https://ebvufind01.dmz1.zbw.eu/10005779650