Several tests of model structure developed by Kneip et al. (J Bus Econ Stat 34:435–456, 2016) and Daraio et al. (Econ J 21:170–191, 2018) rely on comparing sample means of two different efficiency estimators, one appropriate under the conditions of the null hypothesis and the other appropriate under the conditions of the alternative hypothesis. These tests rely on central limit theorems developed by Kneip et al. (Econ Theory 31:394–422, 2015) and Daraio et al. (Econ J 21:170–191, 2018), but require that the original sample be split randomly into two independent subsamples. This introduces some ambiguity surrounding the sample-split, which may be determined by choice of a seed for a random number generator. We develop a method that eliminates much of this ambiguity by repeating the random splits a large number of times. We use a bootstrap algorithm to exploit the information from the multiple sample-splits. Our simulation results show that in many cases, eliminating this ambiguity results in tests with better size and power than tests that employ a single sample-split.

中文翻译：

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使用多个样本分割的非参数生产模型中的假设检验

Kneip等人开发的几种模型结构测试。（J Bus Econ Stat 34：435–456，2016）和Daraio等人。（Econ J 21：170–191，2018）依靠比较两种不同效率估计量的样本均值，一种在原假设的条件下适用，另一种在替代假设的条件下适用。这些测试依赖于Kneip等人开发的中心极限定理。（Econ Theory 31：394–422，2015）和Daraio等人。（Econ J 21：170–191，2018），但要求将原始样本随机分为两个独立的子样本。这在样本分割周围引入了一些歧义，这可以通过为随机数生成器选择种子来确定。我们开发了一种方法，可以通过重复多次随机拆分来消除大部分歧义。我们使用自举算法来利用来自多个样本分割的信息。我们的仿真结果表明，在许多情况下，消除这种歧义会导致测试的大小和功能比采用单个样本拆分的测试更好。