Stochastic Frontier meets Breakdown Frontier

TL;DR

This paper introduces methods for sensitivity analysis in Stochastic Frontier Models, including relaxations of assumptions and the derivation of a breakdown frontier for average inefficiency, with an application and available code.

Contribution

It develops new sensitivity analysis tools for Stochastic Frontier Models, including the breakdown frontier for average inefficiency, enhancing robustness assessments.

Findings

Derived the identified set under relaxed assumptions.

Calculated the breakdown frontier for average inefficiency.

Applied procedures to a well-known dataset with available code.

Abstract

This paper studies sensitivity analysis of Stochastic Frontier Models. We elaborate relaxations of the baseline assumptions in the Stochastic Frontier Models and characterize the identified set under this relaxations. Furthermore, we derive the breakdown frontier for a relevant parameter of interest, the average inefficiency of a production unit. We show an application of the procedures on a well known dataset, and make the code available for the interested practitioner.