Nonparametric Frontier Estimation from Noisy Data

Maik Schwarz (), Sébastien Van Bellegem () and Jean-Pierre Florens ()
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Maik Schwarz: Université catholique de Louvain, Institut de statistique, biostatistique et sciences actuarielles
Sébastien Van Bellegem: University of Toulouse, Toulouse School of Economics (GREMAQ)
Jean-Pierre Florens: University of Toulouse, Toulouse School of Economics (GREMAQ)

Chapter Chapter 3 in Exploring Research Frontiers in Contemporary Statistics and Econometrics, 2011, pp 45-64 from Springer

Abstract: Abstract A new nonparametric estimator of production frontiers is defined and studied when the data set of production units is contaminated by measurement error. The measurement error is assumed to be an additive normal random variable on the input variable, but its variance is unknown. The estimator is a modification of the m-frontier, which necessitates the computation of a consistent estimator of the conditional survival function of the input variable given the output variable. In this paper, the identification and the consistency of a new estimator of the survival function is proved in the presence of additive noise with unknown variance. The performance of the estimator is also studied using simulated data.

Keywords: Data Envelopment Analysis; Survival Function; Production Unit; Consistent Estimator; Production Frontier (search for similar items in EconPapers)
Date: 2011
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DOI: 10.1007/978-3-7908-2349-3_3

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