Wild Bootstrap and Asymptotic Inference With Multiway Clustering
研究了二维聚类回归模型中两种聚类稳健方差估计量，给出t统计量渐近有效的条件，并提出多种野自助法，模拟表明某些自助法表现良好。
We study two cluster-robust variance estimators (CRVEs) for regression models with clustering in two dimensions and give conditions under which t-statistics based on each of them yield asymptotically valid inferences. In particular, one of the CRVEs requires stronger assumptions about the nature of the intra-cluster correlations. We then propose several wild bootstrap procedures and state conditions under which they are asymptotically valid for each type of t-statistic. Extensive simulations suggest that using certain bootstrap procedures with one of the t-statistics generally performs very well. An empirical example confirms that bootstrap inferences can differ substantially from conventional ones.