Parametric covariance matrix modeling in Bayesian panel regression

Mickael Salabasis ()
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Mickael Salabasis: UC AB, Postal: Analyssektionen, SE-117 88 Stockholm, Sweden
Authors registered in the RePEc Author Service: Mickael Bäckman ()

No 565, Working Paper Series in Economics and Finance from Stockholm School of Economics

Abstract: The full Bayesian treatment of error component models typically relies on data augmentation to produce the required inference. Never stricly necessary a direct approach is always possible though not necessarily practical. The mechanics of direct sampling are outlined and a template for including model uncertainty is described. The needed tools, relying on various Markov chain Monte Carlo techniques, are developed and direct sampling, with and without effect selection, is illustrated.

Keywords: Bayesian panel regression; parametric covariance; model selection (search for similar items in EconPapers)
JEL-codes: C11 C33 C63 (search for similar items in EconPapers)
Date: 2004-09-17, Revised 2005-02-16
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