On relaxing the distributional assumption of stochastic frontier models

Abstract

Stochastic frontier models have been considered as an alternative to deterministic frontier models in that they attribute the deviation of the output from the production frontier to both measurement error and inefficiency. However, such merit is often dimmed by strong assumptions on the distribution of the measurement error and the inefficiency such as the normal-half normal pair or the normal-exponential pair. Since the distribution of the measurement error is often accepted as being approximately normal, here we show how to estimate various stochastic frontier models with a relaxed assumption on the inefficiency distribution, building on the recent work of Kneip and his coworkers. We illustrate the usefulness of our method with data on Japanese local public hospitals.