Wild Bootstrap Inference for Wildly Different Cluster Sizes

James G. MacKinnon · Matthew D. Webb

458 citations0/0 files storedJournal of Applied Econometrics

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Spatial and Panel Data Analysis Advanced Statistical Methods and Models Advanced Causal Inference Techniques

Abstract

Summary The cluster robust variance estimator (CRVE) relies on the number of clusters being sufficiently large. Monte Carlo evidence suggests that the ‘rule of 42’ is not true for unbalanced clusters. Rejection frequencies are higher for datasets with 50 clusters proportional to US state populations than with 50 balanced clusters. Using critical values based on the wild cluster bootstrap performs much better. However, this procedure fails when a small number of clusters is treated. We explain why CRVE t statistics and the wild bootstrap fail in this case, study the ‘effective number’ of clusters and simulate placebo laws with dummy variable regressors. Copyright © 2016 John Wiley & Sons, Ltd.

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References

2008Bootstrap-Based Improvements for Inference with Clustered Errors 1990An Illustration of a Pitfall in Estimating the Effects of Aggregate Variables on Micro Units 2007Inference with Difference-in-Differences and Other Panel Data

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