快速可靠的刀切法和Bootstrap法的稳健推理
Fast and Reliable Jackknife and Bootstrap Methods for Cluster-Robust Inference
摘要：
我们提供了计算上有吸引力的方法来获得基于折刀的集群稳健方差矩阵估计（CRVE）的线性回归模型估计的最小二乘。我们还提出了几个新的变种的野生集群引导，其中涉及这些CRVE，折刀为基础的引导数据生成过程，或两者兼而有之。广泛的模拟实验表明，新的方法可以提供更可靠的推理比现有的情况下，后者是不值得信赖的，如当集群的数量是小的和/或集群大小变化很大。三个实证例子说明了新的方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
