Nonparametric Frontier Estimation from Noisy Data
AbstractA 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.
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Bibliographic InfoPaper provided by Institut d'Économie Industrielle (IDEI), Toulouse in its series IDEI Working Papers with number 625.
Date of creation: May 2010
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Other versions of this item:
- Florens, Jean-Pierre & Schwarz, Maik & Van Bellegem, Sébastien, 2010. "Nonparametric Frontier Estimation from Noisy Data," TSE Working Papers 10-179, Toulouse School of Economics (TSE).
- SCHWARZ, Maik & VAN BELLEGEM, Sébastien & FLORENS, Jean - Pierre, 2010. "Nonparametric frontier estimation from noisy data," CORE Discussion Papers 2010050, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models
- P42 - Economic Systems - - Other Economic Systems - - - Productive Enterprises; Factor and Product Markets; Prices
This paper has been announced in the following NEP Reports:
- NEP-ALL-2010-09-18 (All new papers)
- NEP-ECM-2010-09-18 (Econometrics)
- NEP-EFF-2010-09-18 (Efficiency & Productivity)
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