Nonparametric frontier estimation from noisy data

Maik Schwarz (), Sebastien Van Bellegem () and Jean - Pierre Florens
Additional contact information
Maik Schwarz: Université catholique de Louvain, Institut de Statistique, de Biostatistique et de Sciences Actuarielles, B-1348 Louvain-la-Neuve, Belgium
Jean - Pierre Florens: Toulouse School of Economics, France

No 2010050, LIDAM Discussion Papers CORE from Université catholique de Louvain, Center for Operations Research and Econometrics (CORE)

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 through simulated data.

Keywords: production frontier; deconvolution; measurement error; efficiency analysis (search for similar items in EconPapers)
JEL-codes: C14 C24 P42 (search for similar items in EconPapers)
Date: 2010-08-01
New Economics Papers: this item is included in nep-eff
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Citations: View citations in EconPapers (5)

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Related works:
Working Paper: Nonparametric Frontier Estimation from Noisy Data (2010)
Working Paper: Nonparametric Frontier Estimation from Noisy Data (2010)
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Persistent link: https://EconPapers.repec.org/RePEc:cor:louvco:2010050

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