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摘要翻译：
本文研究了随机前沿模型中误差分量的尾部行为，其中一个分量在一侧有界支撑，另一个分量在两侧有无界支撑。在误差分量的弱假设下，我们得到了无界分量分布具有细尾和分量尾等价的非参数检验。检验是随机前沿分析的有用诊断工具。本文对1998-2005年美国6100家银行的随机成本前沿进行了模拟研究和应用。新的检验拒绝正态分布或拉普拉斯分布假设，这是在现有文献中通常强加的。
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英文标题：
《Nonparametric Tests of Tail Behavior in Stochastic Frontier Models》
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作者：
William, C. Horrace and Yulong Wang
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最新提交年份：
2020
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分类信息：
一级分类：Economics 经济学
二级分类：Econometrics 计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
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.
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PDF链接：
https://arxiv.org/pdf/2006.07780
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