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Bayesian Inference on Garch Models Using the Gibbs Sampler

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Author Info
Bauwens, L.
Lubrano, M.

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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.

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Publisher Info
Paper provided by Catholique de Louvain - Center for Operations Research and Economics in its series Papers with number 9627.

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Length: 24 pages
Date of creation: 1996
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Handle: RePEc:fth:louvco:9627

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Keywords: MATHEMATICS ECONOMETRICS

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Find related papers by JEL classification:
C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - General
C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Bayesian Analysis
C19 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Other

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