Wild bootstrap and asymptotic inference with multiway clustering

Arbeitspapier

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.

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• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Queen’s Economics Department Working Paper ; No. 1415

Classification and Topics

• Classification: Wirtschaft ; Statistical Simulation Methods: General ; Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions ; Single Equation Models; Single Variables: Panel Data Models; Spatio-temporal Models
• Subject: CRVE ; grouped data ; clustered data ; cluster-robust variance estimator ; two-way clustering ; robust inference ; wild cluster bootstrap

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): MacKinnon, James G. ; Nielsen, Morten Ørregaard ; Webb, Matthew
• Event: Veröffentlichung
• (who): Queen's University, Department of Economics
• (where): Kingston (Ontario)
• (when): 2019

Further information

• Handle: http://hdl.handle.net/10419/230568