Stochastic FDH/DEA estimators for frontier analysis

Simar, Leopold and Zelenyuk, Valentin (2011) Stochastic FDH/DEA estimators for frontier analysis. Journal of Productivity Analysis, 36 1: 1-20. doi:10.1007/s11123-010-0170-6

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Author Simar, Leopold
Zelenyuk, Valentin
Title Stochastic FDH/DEA estimators for frontier analysis
Journal name Journal of Productivity Analysis   Check publisher's open access policy
ISSN 0895-562X
1573-0441
Publication date 2011-08
Year available 2010
Sub-type Article (original research)
DOI 10.1007/s11123-010-0170-6
Open Access Status
Volume 36
Issue 1
Start page 1
End page 20
Total pages 20
Place of publication Secaucus, NJ, United States
Publisher Springer New York LLC
Collection year 2012
Language eng
Abstract In this paper we extend the work of Simar (J Product Ananl 28:183-201, 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, like Data Envelopment Analysis (DEA), Free Disposal Hull (FDH), etc.... The methodology is based on the theory of local maximum likelihood estimation and extends recent works of Kumbhakar et al. (J Econom 137(1):1-27, 2007) and Park et al. (J Econom 146:185-198, 2008). 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 estimators. The procedure shows great performance for various simulated cases and is also illustrated for some real data sets. Even in the single-output case, our simulated examples show that our stochastic DEA/FDH improves the Kumbhakar et al. (J Econom 137(1):1-27, 2007) method, by making the resulting frontier smoother, monotonic and, if we wish, concave.
Keyword Local maximum likelihood
Nonparametric frontier
Stochastic DEA
Stochastic frontier
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status Non-UQ
Additional Notes Published online: 14 April 2010

Document type: Journal Article
Sub-type: Article (original research)
Collections: Non HERDC
School of Economics Publications
 
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Citation counts: TR Web of Science Citation Count  Cited 22 times in Thomson Reuters Web of Science Article | Citations
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Created: Wed, 18 May 2011, 14:37:48 EST by Alys Hohnen on behalf of School of Economics