🤖 AI Summary
This study addresses the sensitivity of average production inefficiency estimates in stochastic frontier models to benchmark assumptions by introducing, for the first time, the breakdown frontier approach into this framework. By relaxing key identifying assumptions, the paper characterizes the identified set of parameters and derives the breakdown frontier for the parameter of interest to quantify the boundary of misspecification bias. Integrating identified set analysis with sensitivity analysis techniques, the proposed method is validated on classical empirical datasets, demonstrating its effectiveness in assessing robustness. To facilitate reproducibility and practical application, the authors publicly release their implementation code, providing researchers and practitioners with a transparent and accessible tool for evaluating the robustness of inefficiency estimates under potential model misspecification.
📝 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.