Daouia, Abdelaati, Florens, Jean-Pierre and Simar, Léopold (2020) Robust frontier estimation from noisy data : a Tikhonov regularization approach. Econometrics and Statistics, vol. 14. pp. 1-23.

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Identification Number : 10.1016/j.ecosta.2018.07.003


In stochastic frontier models, the regression function defines the production frontier and the regression errors are assumed to be composite. The actually observed outputs are assumed to be contaminated by a stochastic noise. The additive regression errors are composed from this noise term and the one-sided inefficiency term. The aim is to construct a robust nonparametric estimator for the production function. The main tool is a robust concept of partial, expected maximum production frontier, defined as a special probability-weighted moment. In contrast to the deterministic one-sided error model where robust partial frontier modeling is fruitful, the composite error problem requires a substantial different treatment based on deconvolution techniques. To ensure the identifiability of the model, it is sufficient to assume an independent Gaussian noise. In doing so, the frontier estimation necessitates the computation of a survival function estimator from an illposed equation. A Tikhonov regularized solution is constructed and nonparametric frontier estimation is performed. The asymptotic properties of the obtained survival function and frontier estimators are established. Practical guidelines to effect the necessary computations are described via a simulated example. The usefulness of the approach is discussed through two concrete data sets from the sector of Delivery Services.

Item Type: Article
Language: English
Date: 27 April 2020
Refereed: Yes
Uncontrolled Keywords: Deconvolution, Nonparametric estimation, Probability-weighted moment, Production function, Robustness, Stochastic frontie, Tikhonov regularization
JEL Classification: C1 - Econometric and Statistical Methods - General
C13 - Estimation
C14 - Semiparametric and Nonparametric Methods
C49 - Other
Divisions: TSE-R (Toulouse)
Site: UT1
Date Deposited: 27 Aug 2018 13:24
Last Modified: 02 Mar 2023 08:33
OAI Identifier: oai:tse-fr.eu:32889
URI: https://publications.ut-capitole.fr/id/eprint/26160

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