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Methodology and theory for the bootstrap

In: Handbook of Econometrics

Author

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  • Hall, Peter

Abstract

A brief account is given of the methodology and theory for the bootstrap. Methodology is developed in the context of the "equation" approach, which allows attention to be focussed on specific criteria for excellence, such as coverage error of a confidence interval or expected value of a bias-corrected estimator. This approach utilizes a definition of the bootstrap in which the key component is replacing a true distribution function by its empirical estimator. Our theory is Edgeworth expansion based, and is aimed specifically at elucidating properties of different methods for constructing bootstrap confidence intervals in a variety of settings. The reader interested in more detail than can be provided here is referred to the recent monograph of Hall (1992).

Suggested Citation

  • Hall, Peter, 1986. "Methodology and theory for the bootstrap," Handbook of Econometrics, in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 39, pages 2341-2381, Elsevier.
  • Handle: RePEc:eee:ecochp:4-39
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    Citations

    RePEc Biblio mentions

    As found on the RePEc Biblio, the curated bibliography for Economics:
    1. > Econometrics > Econometric Theory > Bootstrap Methods

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    Cited by:

    1. Demian Pouzo, 2014. "Bootstrap Consistency for Quadratic Forms of Sample Averages with Increasing Dimension," Papers 1411.2701, arXiv.org, revised Aug 2015.
    2. Michael Lechner, 1999. "Nonparametric bounds on employment and income effects of continuous vocational training in East Germany," Econometrics Journal, Royal Economic Society, vol. 2(1), pages 1-28.
    3. Krzysztof Pytka & Tomasz Kuszewski, 2010. "Ocena skuteczności handlu emisjami w Unii Europejskiej," Gospodarka Narodowa. The Polish Journal of Economics, Warsaw School of Economics, issue 5-6, pages 73-89.

    More about this item

    JEL classification:

    • C39 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Other

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