Stochastic FDH/DEA estimators for frontier analysis
AbstractIn this paper we extend the work of Simar (2007) introducing noise in nonparametric frontier models. We develop an approach that synthesizes the best features of the two main methods in the estimation of production efficiency. Specifically, our approach first allows for statistical noise, similar to Stochastic Frontier Analysis (even in a more flexible way), and second, it allows modelling multiple-inputs-multiple-outputs technologies without imposing parametric assumptions on production relationship, similar to what is done in non-parametric methods (DEA, FDH, etc. . . ). The methodology is based on the theory of local maximum likelihood estimation and extends recent works of Park, Kumbhakar, Simar and Tsionas (2007) and Park, Simar and Zelenyuk (2006). Our method is suitable for modelling and estimation of the marginal effects onto inefficiency level jointly with estimation of marginal effects of input. The approach is robust to heteroskedastic cases and to various (unknown) distributions of statistical noise and inefficiency, despite assuming simple anchorage models. The method also improves DEA/FDH estimators, by allowing them to be quite robust to statistical noise and especially to outliers, which were the main problems of the original DEA/FDH. The procedure shows great performance for various simulated cases and is also illustrated for some real data sets.
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Bibliographic InfoArticle provided by Springer in its journal Journal of Productivity Analysis.
Volume (Year): 36 (2011)
Issue (Month): 1 (August)
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Web page: http://www.springerlink.com/link.asp?id=100296
Stochastic frontier; Nonparametric frontier; Local maximum likelihood; Stochastic DEA; C13; C14; C2;
Other versions of this item:
- Leopold Simar & Valentin Zelenyuk, 2008. "Stochastic FDH/DEA estimators for Frontier Analysis," Discussion Papers 8, Kyiv School of Economics.
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
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