Fast and reliable jackknife and bootstrap methods for cluster‐robust inference
提出了计算高效的刀切法聚类稳健方差矩阵估计量，以及几种新的野聚类自助法变体，在聚类数少或大小差异大时比现有方法更可靠。
Summary We provide computationally attractive methods to obtain jackknife‐based cluster‐robust variance matrix estimators (CRVEs) for linear regression models estimated by least squares. We also propose several new variants of the wild cluster bootstrap, which involve these CRVEs, jackknife‐based bootstrap data‐generating processes, or both. Extensive simulation experiments suggest that the new methods can provide much more reliable inferences than existing ones in cases where the latter are not trustworthy, such as when the number of clusters is small and/or cluster sizes vary substantially. Three empirical examples illustrate the new methods.