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.
Deconvolution; Nonparametric estimation; Probability-weighted moment; Production function; Robustness; Stochastic frontie; Tikhonov regularization;
- C1: Econometric and Statistical Methods and Methodology: General
- C13: Estimation: General
- C14: Semiparametric and Nonparametric Methods: General
- C49: Other
Abdelaati Daouia, Jean-Pierre Florens, and Léopold Simar, “Robust frontier estimation from noisy data: a Tikhonov regularization approach”, TSE Working Paper, n. 16-665, June 2016, revised July 2018.
Abdelaati Daouia, Jean-Pierre Florens, and Léopold Simar, “Robust frontier estimation from noisy data: a Tikhonov regularization approach”, Econometrics and Statistics, vol. 14, April 2020, pp. 1–23.
Econometrics and Statistics, vol. 14, April 2020, pp. 1–23