存在序列相关时间效应的双向聚类标准误
Standard errors for two-way clustering with serially correlated time effects
AI总结：
该研究针对双向聚类面板提出支持时间效应任意序列相关的标准误与渐近理论，证明其估计量性质，模拟显示置信区间覆盖表现更优，并通过Fama-French三因子回归实证验证实用性。
摘要：
我们针对双向聚类面板提出了改进的标准误和渐近分布理论。我们提出的估计量和理论允许共同时间效应中存在任意序列相关性，这是现有双向方法所不支持的，包括流行的Cameron、Gelbach和Miller（2011）的双向聚类标准误，以及Menzel（2021）的聚类bootstrap法。我们的渐近分布理论是首个允许观测值间存在这种程度相互依赖性的理论。在弱正则条件下，我们证明了最小二乘估计量是渐近正态的，我们提出的方差估计量具有一致性，t比率渐近服从标准正态分布，从而可进行常规推断。我们提供的模拟证据表明，使用我们提出的标准误构建的置信区间，相比现有方法具备更优的覆盖表现。我们通过一项针对标准Fama-French三因子回归的实证应用，阐明了所提方法的实用性。
英文摘要：
We propose improved standard errors and an asymptotic distribution theory for two-way clustered panels. Our proposed estimator and theory allow for arbitrary serial dependence in the common time effects, which is excluded by existing two-way methods, including the popular two-way cluster standard errors of Cameron, Gelbach, and Miller (2011) and the cluster bootstrap of Menzel (2021). Our asymptotic distribution theory is the first which allows for this level of inter-dependence among the observations. Under weak regularity conditions, we demonstrate that the least squares estimator is asymptotically normal, our proposed variance estimator is consistent, and t-ratios are asymptotically standard normal, permitting conventional inference. We present simulation evidence that confidence intervals constructed with our proposed standard errors obtain superior coverage performance relative to existing methods. We illustrate the relevance of the proposed method in an empirical application to a standard Fama-French three-factor regression.
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