双向聚类中具有序列相关时间效应的刀切方差估计量

摘要

Chiang, Hansen, and Sasaki (Chiang et al. (2024)) and Chen and Vogelsang (Chen and Vogelsang (2024)) developed cluster-robust variance estimators (CRVEs) for linear regressions with two-way clustered data allowing for arbitrary serial dependence. However, conventional CRVEs can perform poorly in finite samples. We propose jackknife CRVEs and establish their theoretical validity. Our theoretical and simulation results demonstrate several advantages of the proposed procedures. First, the jackknife CRVE delivers more reliable finite-sample inference than the conventional two-way CRVE. Second, it avoids undefined standard errors arising from non-positive definite CRVEs. Third, in a benchmark setting, it is never downward biased. Finally, when the limiting distribution may be non-Gaussian, the proposed max-jackknife test remains asymptotically conservative.