An Improved Bootstrap Test of Stochastic Dominance
An Improved Bootstrap Test of Stochastic Dominance
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DOI：
10.2139/ssrn.1435460
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发表时间：
2009-07
期刊：
影响因子：
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通讯作者：
O. Linton;Kyungchul Song;Yoon-Jae Whang
中科院分区：
文献类型：
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作者：
O. Linton;Kyungchul Song;Yoon-Jae Whang
我们提出了一种新的方法来测试随机优势，改进现有的测试的基础上，标准的引导或子抽样。该方法承认前景涉及无限以及有限维未知参数，使变量被允许从非参数和半参数模型的残差。建议的自助测试的渐近大小小于或等于标称水平均匀的概率在零假设下的规律性条件。本文还刻画了渐近大小一致地恰好等于标称水平的概率集。正如我们的模拟结果所示，我们的测试的这些特性导致一般的改善的功率特性。改进源于自举检验的设计，其极限行为模仿了原始检验极限分布的不连续性。
We propose a new method of testing stochastic dominance that improves on existing tests based on the standard bootstrap or subsampling. The method admits prospects involving infinite as well as finite dimensional unknown parameters, so that the variables are allowed to be residuals from nonparametric and semiparametric models. The proposed bootstrap tests have asymptotic sizes that are less than or equal to the nominal level uniformly over probabilities in the null hypothesis under regularity conditions. This paper also characterizes the set of probabilities that the asymptotic size is exactly equal to the nominal level uniformly. As our simulation results show, these characteristics of our tests lead to an improved power property in general. The improvement stems from the design of the bootstrap test whose limiting behavior mimics the discontinuity of the original test's limiting distribution.