An Information Theoretic Approach to Flexible Stochastic Frontier Models

Doulgas Miller ()
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Doulgas Miller: Department of Economics, University of Missouri-Columbia

No 717, Working Papers from Department of Economics, University of Missouri

Abstract: Parametric stochastic frontier models have a long history in applied production eco- nomics, but the class of tractible parametric models is relatively small. Consequently, researchers have recently considered nonparametric 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.

Keywords: KullbackLeibler information criterion; output distance function; profile likelihood; stochastic frontier; technical efficiency (search for similar items in EconPapers)
JEL-codes: C13 C21 C51 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-eff
Date: Written 2007-07-16
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