Nonparametric Frontier Estimation from Noisy Data

Jean-Pierre Florens, Maik Schwarz and Sebastien Van Bellegem ()

No 10-179, TSE Working Papers from Toulouse School of Economics (TSE)

Abstract: A new nonparametric estimator of production a frontier 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.

Date: 2010-05
New Economics Papers: this item is included in nep-ecm, nep-eff and nep-ore
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Citations: View citations in EconPapers (4)

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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:tse:wpaper:22897

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