Parametric covariance matrix modeling in Bayesian panel regression
Mickael Salabasis ()
Additional contact information
Mickael Salabasis: UC AB, Postal: Analyssektionen, SE-117 88 Stockholm, Sweden
Authors registered in the RePEc Author Service: Mickael Bäckman ()
No 565, SSE/EFI Working Paper Series in Economics and Finance from Stockholm School of Economics
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)
Pages: 23 pages
Date: 2004-09-17, Revised 2005-02-16
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
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:hhs:hastef:0565
Access Statistics for this paper
More papers in SSE/EFI Working Paper Series in Economics and Finance from Stockholm School of Economics The Economic Research Institute, Stockholm School of Economics, P.O. Box 6501, 113 83 Stockholm, Sweden. Contact information at EDIRC.
Bibliographic data for series maintained by Helena Lundin ().