论文标题
随机前沿模型中尾巴行为的非参数测试
Nonparametric Tests of Tail Behavior in Stochastic Frontier Models
论文作者
论文摘要
本文研究了随机前沿模型中误差组件的尾部行为，其中一个组件在一侧有界限，而另一侧则在双方都没有任何支持。在误差组件上的弱假设下，我们得出了非参数测试，即无界的组件分布具有较薄的尾部，并且组件尾巴是等效的。测试是用于随机边界分析的有用诊断工具。从1998年到2005年，提供了一项模拟研究和对随机成本前沿的应用。新测试拒绝正常或拉普拉斯分布假设，这些假设通常在现有文献中施加。
This article studies tail behavior for the error components in the stochastic frontier model, where one component has bounded support on one side, and the other has unbounded support on both sides. Under weak assumptions on the error components, we derive nonparametric tests that the unbounded component distribution has thin tails and that the component tails are equivalent. The tests are useful diagnostic tools for stochastic frontier analysis. A simulation study and an application to a stochastic cost frontier for 6,100 US banks from 1998 to 2005 are provided. The new tests reject the normal or Laplace distributional assumptions, which are commonly imposed in the existing literature.
