Wild Bootstrap and Asymptotic Inference With Multiway Clustering
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.
|Keywords||Cluster-robust variance estimator, Clustered data, CRVE, Grouped data, Robust inference, Wild cluster bootstrap|
|Journal||Journal of Business and Economic Statistics|
MacKinnon, J.G. (James G.), Nielsen, M.Ø. (Morten Ørregaard), & Webb, M. (2019). Wild Bootstrap and Asymptotic Inference With Multiway Clustering. Journal of Business and Economic Statistics. doi:10.1080/07350015.2019.1677473