This study explores the question of adapting a likelihood-ratio test in the two-stage data envelopment analysis (DEA) framework, where DEA estimates are regressed against external factors. We focus on the hypotheses of testing the technological difference across time periods (or groups) and propose two bootstrapping procedures. Our Monte Carlo (MC) simulation shows that the proposed test has a substantially better-estimated size for the case of smoothing the ‘spurious ones’, rather than removing them. MC simulation also confirms the curse of dimensionality when the input and output variables increase in the production model. Finally, the proposed test is demonstrated in an empirical illustration.

中文翻译：

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面板数据的两阶段数据包络分析中技术差异的似然比检验


本研究探讨了在两阶段数据包络分析 （DEA） 框架中调整似然比检验的问题，其中 DEA 估计值与外部因素进行回归。我们专注于测试不同时间段（或组）的技术差异的假设，并提出了两种引导程序。我们的蒙特卡洛 （MC） 仿真表明，对于平滑 “杂散 ”而不是删除它们的情况，所提出的测试具有更好的估计大小。MC 仿真还证实了当生产模型中的输入和输出变量增加时，维度的诅咒。最后，以实证说明证明了所提出的测试。