Wild Bootstrap Inference for Wildly Different Cluster Sizes

By James G. MacKinnon, Matthew D. Webb. 2013

411 citations recorded by the source corpus.

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.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/6afd06af6b2f4ab964783cec39d587cc00f4be8e

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Citations in the corpus

Small-Sample Adjustments for Tests of Moderators and Model Fit Using Robust Variance Estimation in Meta-Regression (2015, methodology): “Thus, we have not considered iterative procedures such as bootstrapping, although some recent work in econometrics has proposed promising techniques for bootstrapping in settings with cluster-dependent observations (Webb & MacKinnon, 2013).”
The Effects of Teacher Match on Students’ Academic Perceptions and Attitudes (2017, background): “Moreover, McKinnon and Webb (2016), Webb (2014), and Cameron and Miller (2015) all caution against using the wild-cluster bootstrap procedure when the number of clusters is either very small or unbalanced, both of which are true in our case.”
Immigration and Wage Dynamics: Evidence from the Mexican Peso Crisis (2020, background): “Precise estimates on net Mexican immigration are hard to obtain (see Passel (2005), Passel et al.”
The Wild Bootstrap with a “Small” Number of “Large” Clusters (2019, background): “Finally, there is a large literature studying the properties of variations of the wild bootstrap, including, in addition to some of the already noted references, Webb (2013) and MacKinnon and Webb (2017).”
Wine Prices and Quality Ratings: A Meta-regression Analysis (2014, positive): “A recent study by MacKinnon and Webb (2014) suggests the wild bootstrap may perform well in cases where there is severe inequality between cluster sizes.”
Shock-Based Causal Inference in Corporate Finance and Accounting Research (2015, background): “On the wild cluster bootstrap, ans the likely best available response if one has a small number of clusters, see Cameron et al. (2008) (Stata code cgmwildboot.ado available on Judson Caskey’s website) MacKinnon and Webb (2016).”