Robust maximum likelihood estimation of stochastic frontier models
研究了随机前沿模型中最大似然估计的稳健性，推导了稳健估计的充分条件，发现学生t分布与多种无效率分布搭配能满足条件，且稳健估计对异常值不敏感。
When analysing the efficiency of decision-making units, the robustness of efficiency scores to changes in the data is desirable, especially in the context of managerial or regulatory benchmarking. However, the robustness of maximum likelihood estimation of stochastic frontier models remains underexplored. We examine the behaviour of the influence function of the estimator in a stochastic frontier context, and derive some sufficient conditions for robust maximum likelihood estimation in terms of the properties of the marginal distributions of the error components and, in cases where they are dependent, the copula density. We find that the canonical distributional assumptions do not satisfy these conditions. The Student’s t noise distribution is found to have some particularly attractive properties which means it can be paired with a broad class of inefficiency distributions while still satisfying our conditions under independence. We show that parameter estimates and efficiency predictions from robust specifications are significantly less sensitive to contaminating observations than those from non-robust specifications.