期刊
STATA JOURNAL
卷 19, 期 1, 页码 4-60出版社
SAGE PUBLICATIONS INC
DOI: 10.1177/1536867X19830877
关键词
st0549; boottest; artest; waldtest; scoretest; Anderson-Rubin test; Wald test; wild bootstrap; wild cluster bootstrap; score bootstrap; multiway clustering; few treated clusters
资金
- Social Sciences and Humanities Research Council of Canada
- Canada Research Chairs program
- Social Sciences and Humanities Research Council
- Center for Research in Econometric Analysis of Time Series (CREATES)
- Danish National Research Foundation [DNRF78]
The wild bootstrap was originally developed for regression models with heteroskedasticity of unknown form. Over the past 30 years, it has been extended to models estimated by instrumental variables and maximum likelihood and to ones where the error terms are (perhaps multiway) clustered. Like bootstrap methods in general, the wild bootstrap is especially useful when conventional inference methods are unreliable because large-sample assumptions do not hold. For example, there may be few clusters, few treated clusters, or weak instruments. The package boottest can perform a wide variety of wild bootstrap tests, often at remarkable speed. It can also invert these tests to construct confidence sets. As a postestimation command, boottest works after linear estimation commands, including regress, cnsreg, ivregress, ivreg2, areg, and reghdfe, as well as many estimation commands based on maximum likelihood. Although it is designed to perform the wild cluster bootstrap, boottest can also perform the ordinary (nonclustered) version. Wrappers offer classical Wald, score/Lagrange multiplier, and Anderson-Rubin tests, optionally with (multiway) clustering. We review the main ideas of the wild cluster bootstrap, offer tips for use, explain why it is particularly amenable to computational optimization, state the syntax of boottest, artest, scoretest, and waldtest, and present several empirical examples.
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