Nonparametric tests of tail behavior in stochastic frontier models
研究了随机前沿模型中误差成分的尾部行为，提出了非参数检验方法来判断是否服从薄尾分布（如正态或拉普拉斯），并通过模拟和四个数据集验证了其有效性。
Summary 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 for thin‐tailed distributional assumptions imposed on these two components. The tests are useful diagnostic tools for stochastic frontier analysis and kernel deconvolution density estimation. A simulation study and applications to four previously studied datasets are provided. In two of these applications, the new tests reject the thin‐tailed distributional assumptions such as normal or Laplace, which are commonly imposed in the existing literature.