An Information Theoretic Approach to Flexible Stochastic Frontier Models
AbstractParametric 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.
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Bibliographic InfoPaper provided by Department of Economics, University of Missouri in its series Working Papers with number 0717.
Length: 26 pgs.
Date of creation: 16 Jul 2007
Date of revision:
KullbackLeibler information criterion; output distance function; profile likelihood; stochastic frontier; technical efficiency;
Find related papers by JEL classification:
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
This paper has been announced in the following NEP Reports:
- NEP-ALL-2007-09-02 (All new papers)
- NEP-ECM-2007-09-02 (Econometrics)
- NEP-EFF-2007-09-02 (Efficiency & Productivity)
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