Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/56184
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dc.contributor.authorSalabasis, Mickaelen_US
dc.date.accessioned2012-03-28T13:05:37Z-
dc.date.available2012-03-28T13:05:37Z-
dc.date.issued2004en_US
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_US
dc.language.isoengen_US
dc.publisher|aEkonomiska Forskningsinst. |cStockholmen_US
dc.relation.ispartofseries|aSSE/EFI Working Paper Series in Economics and Finance |x565en_US
dc.subject.jelC11en_US
dc.subject.jelC33en_US
dc.subject.jelC63en_US
dc.subject.ddc330en_US
dc.subject.keywordBayesian panel regressionen_US
dc.subject.keywordparametric covarianceen_US
dc.subject.keywordmodel selectionen_US
dc.subject.stwBayes-Statistiken_US
dc.subject.stwPanelen_US
dc.subject.stwNichtparametrisches Verfahrenen_US
dc.subject.stwTheorieen_US
dc.titleParametric covariance matrix modeling in Bayesian panel regressionen_US
dc.typeWorking Paperen_US
dc.identifier.ppn479830266en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen_US

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