A new approach to stochastic frontier estimation: DEA+

Dieter Gstach ()
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Dieter Gstach: Department of Economics, Vienna University of Economics & B.A.

Department of Economics Working Papers from Vienna University of Economics and Business, Department of Economics

Abstract: The outcome of a production process might not only deviate from a theoretical maximum due to inefficiency, but also because of non-controllable influences. This raises the issue of reliability of Data Envelopment Analysis in noisy environments. I propose to assume an i.i.d. data generating process with bounded noise component, so that the following approach is feasible: Use DEA to estimate a pseudo frontier first (nonparametric shape estimation). Next apply a ML- technique to the DEA-estimated efficiencies, to estimate the scalar value by which this pseudo-frontier must be shifted downward to get the true production frontier (location estimation). I prove, that this approach yields consistent estimates of the true frontier.

Keywords: Stochastic DEA; Consistency; Semi-Parametric Frontier Estimation; MLE (search for similar items in EconPapers)
JEL-codes: C14 C24 D24 (search for similar items in EconPapers)
Date: 1996-06
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Citations: View citations in EconPapers (30)

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