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dc.contributor.authorMiller, Douglas J.eng
dc.description.abstractParametric stochastic frontier models have a long history in applied production economics, but the class of tractible parametric models is relatively small. Consequently, researchers have recently considered non-parametric alternatives such as kernel den- sity estimators, functional approximations, and data envelopment analysis (DEA). The purpose of this paper is to present an information theoretic approach to constructing more flexible classes of parametric stochastic frontier models. Further, the proposed class of models nests all of the commonly used parametric methods as special cases, and the proposed modeling framework provides a comprehensive means to conduct model specification tests. The modeling framework is also extended to develop information theoretic measures of mean technical efficiency and to construct a profile likelihood estimator of the stochastic frontier model.eng
dc.identifier.citationDepartment of Economics, 2007eng
dc.publisherDepartment of Economicseng
dc.relation.ispartofEconomics publicationseng
dc.relation.ispartofcommunityUniversity of Missouri-Columbia. College of Arts and Sciences. Department of Economicseng
dc.relation.ispartofseriesWorking papers (Department of Economics);WP 07-17eng
dc.rights.licenseThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License.
dc.subjectKullback-Leibler information criterioneng
dc.subjectoutput distance functioneng
dc.subjectprofile likelihoodeng
dc.subjectstochastic frontiereng
dc.subjecttechnical efficiencyeng
dc.subject.lcshStochastic processeseng
dc.subject.lcshEconometrics -- Mathematical modelseng
dc.titleAn Information Theoretic Approach to Flexible Stochastic Frontier Modelseng
dc.typeWorking Papereng

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