Wild Bootstrap and Asymptotic Inference With Multiway Clustering
By James G. MacKinnon, Morten Ørregaard Nielsen, Matthew D. Webb. Journal of business & economic statistics, 2019
74 citations recorded by the source corpus.
Abstract 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.
Subfields: Demography, Public Health, Environmental and Occupational Health, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/572fa79f7053197dadb1d664226c3ba50cd8f2bc
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Demography
In Public Health, Environmental and Occupational Health
In Statistics and Probability
Citations in the corpus
- Empirical process results for exchangeable arrays (2019, methodology): “Regarding multiway clustering, the only papers we are aware of are the recent works of Menzel (2018) and MacKinnon et al. (2019).1 Again, they focus on linear parameters.2 In this paper, we establish uniform laws of large numbers (LLN) and central limit theorems (CLT) for such type of data.”