Wild Bootstrap Inference for Wildly Different Cluster Sizes
Wild Bootstrap Inference for Wildly Different Cluster Sizes
复制标题
针对截然不同的簇大小的狂野引导推理
DOI：
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复制
发表时间：
2017
期刊：
影响因子：
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通讯作者：
Matthew D. Webb
中科院分区：
文献类型：
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作者：
J. MacKinnon;Matthew D. Webb
总结
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.