Fast And Wild: Bootstrap Inference In Stata Using Boottest
Matthew Webb and
No 1406, Working Paper from Economics Department, Queen's University
The wild bootstrap was originally developed for regression models with heteroskedasticity of unknown form. Over the past thirty years, it has been extended to models estimated by instrumental variables and maximum likelihood, and to ones where the error terms are (perhaps multi-way) clustered. Like bootstrap methods in general, the wild bootstrap is especiallyuseful 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 Stata 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 (non-clustered) version. Wrappers offer classical Wald, score/LM, and Anderson-Rubin tests, optionally with (multi-way) clustering. We review the main ideasof 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 for illustration.
Keywords: artest; Anderson-Rubin test; Wald test; wild bootstrap; wild cluster bootstrap; score bootstrap; multi-way clustering; few treated clusters; boottest; waldtest (search for similar items in EconPapers)
JEL-codes: C15 C21 C23 C25 C36 (search for similar items in EconPapers)
Pages: 48 pages
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Journal Article: Fast and wild: Bootstrap inference in Stata using boottest (2019)
Working Paper: Fast and Wild: Bootstrap Inference in Stata Using boottest (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:qed:wpaper:1406
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