IDEAS home Printed from https://ideas.repec.org/h/eee/ecochp/5-52.html
   My bibliography  Save this book chapter

The Bootstrap

In: Handbook of Econometrics

Author

Listed:
  • Horowitz, Joel L.

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. The bootstrap is a practical technique that is ready for use in applications. This chapter explains and illustrates the usefulness and limitations of the bootstrap in contexts of interest in econometrics. The chapter outlines the theory of the bootstrap, provides numerical illustrations of its performance, and gives simple instructions on how to implement the bootstrap in applications. The presentation is informal and expository. Its aim is to provide an intuitive understanding of how the bootstrap works and a feeling for its practical value in econometrics.

Suggested Citation

  • Horowitz, Joel L., 2001. "The Bootstrap," Handbook of Econometrics,in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 52, pages 3159-3228 Elsevier.
  • Handle: RePEc:eee:ecochp:5-52
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/B7GX7-4DXJCWR-1C/2/7ad1350e542d568d67eeab9e81ce1642
    Download Restriction: Full text for ScienceDirect subscribers only

    As the access to this document is restricted, you may want to search for a different version of it.

    More about this item

    JEL classification:

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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecochp:5-52. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu). General contact details of provider: http://www.elsevier.com/wps/find/bookseriesdescription.cws_home/BS_HE/description .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.