Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/56184 
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dc.contributor.authorSalabasis, Mickaelen
dc.date.accessioned2012-03-28T13:05:37Z-
dc.date.available2012-03-28T13:05:37Z-
dc.date.issued2004-
dc.identifier.urihttp://hdl.handle.net/10419/56184-
dc.description.abstractThe 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.en
dc.language.isoengen
dc.publisher|aStockholm School of Economics, The Economic Research Institute (EFI) |cStockholmen
dc.relation.ispartofseries|aSSE/EFI Working Paper Series in Economics and Finance |x565en
dc.subject.jelC11en
dc.subject.jelC33en
dc.subject.jelC63en
dc.subject.ddc330en
dc.subject.keywordBayesian panel regressionen
dc.subject.keywordparametric covarianceen
dc.subject.keywordmodel selectionen
dc.subject.stwBayes-Statistiken
dc.subject.stwPanelen
dc.subject.stwNichtparametrisches Verfahrenen
dc.subject.stwTheorieen
dc.titleParametric covariance matrix modeling in Bayesian panel regression-
dc.typeWorking Paperen
dc.identifier.ppn479830266en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen

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