Publication:
Stochastic frontier models: a bayesian perspective

dc.affiliation.dptoUC3M. Departamento de Economíaes
dc.contributor.authorKoop, Gary
dc.contributor.authorOsiewalski, Jacek
dc.contributor.authorSteel, Mark F.J.
dc.contributor.authorBroeck, Julien Van den
dc.contributor.editorUniversidad Carlos III de Madrid. Departamento de Economía
dc.date.accessioned2008-08-21T08:10:22Z
dc.date.available2008-08-21T08:10:22Z
dc.date.issued1992-04
dc.description.abstractA Bayesian approach to estimation, prediction and model comparison in composed error production models is presented. A broad range of distributions on the inefficiency term define the contending models, which can either be treated separately or pooled. Posterior results are derived for the individual efficiencies as well as for the parameters, and the differences with the usual sampling-theory approach are highlighted. The required numerical integrations are handled by Monte Carlo methods with Importance Sampling, and an empirical example illustrates the procedures.
dc.format.mimetypeapplication/pdf
dc.identifier.issn2340-5031
dc.identifier.urihttps://hdl.handle.net/10016/2823
dc.language.isoeng
dc.relation.ispartofseriesWorking Papers
dc.relation.ispartofseries1992-12
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.ecienciaEconomía
dc.subject.otherEfficiency
dc.subject.otherComposed error models
dc.subject.otherProduction frontier
dc.subject.otherPrior elicitation
dc.titleStochastic frontier models: a bayesian perspective
dc.typeworking paper*
dspace.entity.typePublication
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