Robust Estimation in Stochastic Frontier Models

TL;DR

This paper introduces a robust estimator for stochastic frontier models that maintains strong statistical properties and robustness, demonstrated through simulations and real data analysis.

Contribution

It integrates Basu et al.'s robust estimation approach into stochastic frontier models, ensuring consistency, normality, and robustness with minimal efficiency loss.

Findings

Estimator is strongly consistent and asymptotically normal.

Demonstrates robustness with little efficiency loss in simulations.

Effective in real data application.

Abstract

This study proposes a robust estimator for stochastic frontier models by integrating the idea of Basu et al. [1998, Biometrika 85, 549-559] into such models. We verify that the suggested estimator is strongly consistent and asymptotic normal under regularity conditions and investigate robust properties. We use a simulation study to demonstrate that the estimator has strong robust properties with little loss in asymptotic efficiency relative to the maximum likelihood estimator. A real data analysis is performed for illustrating the use of the estimator.