Working papers, Department of Economics, WU (Wirtschaftsuniversität Wien)
A new approach to stochastic frontier estimation: DEA+
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; (follow links to similar papers)
JEL-Codes: C14; C24; D24; (follow links to similar papers)
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