论文标题
具有高维协变量的异性抗性性能过度识别限制性测试
A Heteroskedasticity-Robust Overidentifying Restriction Test with High-Dimensional Covariates
论文作者
论文摘要
本文提出了高维线性仪器变量模型的过度识别限制性测试。提出的测试的新颖性是，它允许协变量和仪器的数量大于样本量。该测试是尺度不变的，对于异方差错误是可靠的。为了构建最终测试统计量，我们首先基于可能具有高维的多个参数的最大规范引入测试。基于最大规范的理论能力高于修改后的Cragg-Donald检验（Kolesár，2018年），这是唯一允许大维协变量的测试。其次，遵循功率增强的原理（Fan等，2015），我们引入了功率增强测试，其渐近零组件用于增强功率，以检测许多局部无效仪器的极端替代方案。最后，贸易和经济增长Nexus的经验例子证明了拟议的测试的有用性。
This paper proposes an overidentifying restriction test for high-dimensional linear instrumental variable models. The novelty of the proposed test is that it allows the number of covariates and instruments to be larger than the sample size. The test is scale-invariant and is robust to heteroskedastic errors. To construct the final test statistic, we first introduce a test based on the maximum norm of multiple parameters that could be high-dimensional. The theoretical power based on the maximum norm is higher than that in the modified Cragg-Donald test (Kolesár, 2018), the only existing test allowing for large-dimensional covariates. Second, following the principle of power enhancement (Fan et al., 2015), we introduce the power-enhanced test, with an asymptotically zero component used to enhance the power to detect some extreme alternatives with many locally invalid instruments. Finally, an empirical example of the trade and economic growth nexus demonstrates the usefulness of the proposed test.
