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Nonparametric Frontier Estimation from Noisy Data

In: Exploring Research Frontiers in Contemporary Statistics and Econometrics

Author

Listed:
  • Maik Schwarz

    (Université catholique de Louvain, Institut de statistique, biostatistique et sciences actuarielles)

  • Sébastien Van Bellegem

    (University of Toulouse, Toulouse School of Economics (GREMAQ)
    Université catholique de Louvain, Center for Operations Research and Econometrics)

  • Jean-Pierre Florens

    (University of Toulouse, Toulouse School of Economics (GREMAQ))

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.

Suggested Citation

  • Maik Schwarz & Sébastien Van Bellegem & Jean-Pierre Florens, 2011. "Nonparametric Frontier Estimation from Noisy Data," Springer Books, in: Ingrid Van Keilegom & Paul W. Wilson (ed.), Exploring Research Frontiers in Contemporary Statistics and Econometrics, chapter 0, pages 45-64, Springer.
    • Handle: RePEc:spr:sprchp:978-3-7908-2349-3_3
    DOI: 10.1007/978-3-7908-2349-3_3
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