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Bootstrap Methods in Econometrics

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  • Joel L. Horowitz

Abstract

The bootstrap is a method for estimating the distribution of an estimator or test statistic by resampling one's data or a model estimated from the data. Under conditions that hold in a wide variety of econometric applications, the bootstrap provides approximations to distributions of statistics, coverage probabilities of confidence intervals, and rejection probabilities of hypothesis tests that are more accurate than the approximations of first-order asymptotic distribution theory. The reductions in the differences between true and nominal coverage or rejection probabilities can be very large. In addition, the bootstrap provides a way to carry out inference in certain settings where obtaining analytic distributional approximations is difficult or impossible. This article explains the usefulness and limitations of the bootstrap in contexts of interest in econometrics. The presentation is informal and expository. It provides an intuitive understanding of how the bootstrap works. Mathematical details are available in the references that are cited.

Suggested Citation

  • Joel L. Horowitz, 2019. "Bootstrap Methods in Econometrics," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 193-224, August.
    • Handle: RePEc:anr:reveco:v:11:y:2019:p:193-224
    DOI: 10.1146/annurev-economics-080218-025651
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    Cited by:

    1. James G. MacKinnon & Matthew D. Webb, 2020. "When and How to Deal with Clustered Errors in Regression Models," Working Paper 1421, Economics Department, Queen's University.
    2. Campbell R. Harvey & Yan Liu, 2022. "Luck versus Skill in the Cross Section of Mutual Fund Returns: Reexamining the Evidence," Journal of Finance, American Finance Association, vol. 77(3), pages 1921-1966, June.
    3. Acconcia Antonio & Beraldo Sergio & Capuano Carlo & Stimolo Marco, 2023. "Public Subsidies and Cooperation in Research and Development. Evidence from the LAB," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 23(3), pages 727-760, July.
    4. Ali Tafti & Galit Shmueli, 2020. "Beyond Overall Treatment Effects: Leveraging Covariates in Randomized Experiments Guided by Causal Structure," Information Systems Research, INFORMS, vol. 31(4), pages 1183-1199, December.
    5. Queirós, Francisco, 2024. "Asset bubbles and product market competition," Theoretical Economics, Econometric Society, vol. 19(1), January.
    6. Wu Wang & Xuming He & Zhongyi Zhu, 2020. "Statistical inference for multiple change‐point models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 47(4), pages 1149-1170, December.
    7. Francisco Queirós, 2024. "The real side of stock market exuberance: bubbles, output and productivity at the industry level," Economica, London School of Economics and Political Science, vol. 91(361), pages 268-291, January.
    8. Claudia Pigini & Alessandro Pionati & Francesco Valentini, 2023. "Specification testing with grouped fixed effects," Papers 2310.01950, arXiv.org.
    9. Jeffrey D. Michler & Anna Josephson, 2022. "Recent developments in inference: practicalities for applied economics," Chapters, in: A Modern Guide to Food Economics, chapter 11, pages 235-268, Edward Elgar Publishing.
    10. Haffar, Adlane & Le Fur, Eric, 2021. "Structural vector error correction modelling of Bitcoin price," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 170-178.
    11. Tianyu Fan & Michael Peters & Fabrizio Zilibotti, 2023. "Growing Like India—the Unequal Effects of Service‐Led Growth," Econometrica, Econometric Society, vol. 91(4), pages 1457-1494, July.
    12. Yu, Lu & Li, Yanglin, 2023. "Testing factor models when asset bubbles occur: A time-varying perspective," Economic Modelling, Elsevier, vol. 124(C).

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