Nonparametric Frontier Estimation from Noisy Data

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.

Reference

Jean-Pierre Florens, Maik Schwarz, and Sébastien Van Bellegem, “Nonparametric Frontier Estimation from Noisy Data”, TSE Working Paper, n. 10-179, May 2010.