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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 Orregard 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 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 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 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 post-estimation 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 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 for illustration.

Suggested Citation

  • David Roodman & James G. MacKinnon & Morten Orregard Nielsen & Matthew D. Webb, 2018. "Fast and Wild: Bootstrap Inference in Stata Using boottest," Working Papers 1406, Queen's University, Department of Economics.
  • Handle: RePEc:qed:wpaper:1406
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    File URL: http://qed.econ.queensu.ca/working_papers/papers/qed_wp_1406.pdf
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    More about this item

    Keywords

    Anderson-Rubin test; Wald test; wild bootstrap; wild cluster bootstrap; score bootstrap; multi-way clustering; few treated clusters; boottest; waldtest; artest;

    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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