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Fast and wild: Bootstrap inference in Stata using boottest

Author

Listed:
  • David Roodman

    () (Open Philanthropy Project)

  • James G. MacKinnon

    () (Queen’s University)

  • Morten Ørregaard Nielsen

    () (Queen’s University)

  • Matthew D. Webb

    () (Carleton University)

Abstract

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 boot- strap 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, in- cluding 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/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.

Suggested Citation

  • David Roodman & James G. MacKinnon & Morten Ørregaard Nielsen & Matthew D. Webb, 2019. "Fast and wild: Bootstrap inference in Stata using boottest," Stata Journal, StataCorp LP, vol. 19(1), pages 4-60, March.
  • Handle: RePEc:tsj:stataj:v:19:y:2019:i:1:p:4-60
    DOI: 10.1177/1536867X19830877
    Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj19-1/st0549/
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    File URL: http://hdl.handle.net/10.1177/1536867X19830877
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    References listed on IDEAS

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    5. Antoine A. Djogbenou & James G. MacKinnon & Morten Ø. Nielsen, 2018. "Asymptotic Theory And Wild Bootstrap Inference With Clustered Errors," Working Paper 1399, Economics Department, Queen's University.
    6. James G. MacKinnon & Morten Ø. Nielsen & Matthew D. Webb, 2019. "Wild Bootstrap and Asymptotic Inference with Multiway Clustering," Working Paper 1415, Economics Department, Queen's University.
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    More about this item

    Keywords

    boottest; artest; waldtest; scoretest; Anderson–Rubin test; Wald test; wild bootstrap; wild cluster bootstrap; score bootstrap; multiway clustering; few treated clusters;

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation

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