A Laplace stochastic frontier model
提出用拉普拉斯分布替代正态分布作为随机前沿模型中的误差项，发现无效率项的条件分布在复合误差为正时恒定、为负时变化，模拟显示模型在误差设定错误时表现优于传统正态-指数模型，并应用于美国航空公司数据。
We propose a Laplace stochastic frontier model as an alternative to the traditional model with normal errors. An interesting feature of the Laplace model is that the distribution of inefficiency conditional on the composed error is constant for positive values of the composed error, but varies for negative values. A simulation study suggests that the model performs well relative to the normal-exponential model when the two-sided error is misspecified. An application to U.S. Airlines is provided.