IDEAS home Printed from https://ideas.repec.org/a/wly/emjrnl/v19y2016i1p33-54.html
   My bibliography  Save this article

Asymptotic refinements of nonparametric bootstrap for quasi‐likelihood ratio tests for classes of extremum estimators

Author

Listed:
  • Lorenzo Camponovo

Abstract

We study the asymptotic refinements of nonparametric bootstrap for quasi‐likelihood ratio type tests of nonlinear restrictions. The bootstrap method applies to extremum estimators, such as quasi‐maximum likelihood and generalized method of moments estimators, among others. Unlike existing parametric bootstrap procedures for quasi‐likelihood ratio type tests, this bootstrap approach does not require any specific parametric assumption on the data distribution, and constructs the bootstrap samples in a fully nonparametric way. We derive the higher‐order improvements of the nonparametric bootstrap compared to procedures based on standard first‐order asymptotic theory. We show that the magnitude of these improvements is the same as those of parametric bootstrap procedures currently proposed in the literature. Monte Carlo simulations confirm the reliability and accuracy of the nonparametric bootstrap.

Suggested Citation

  • Lorenzo Camponovo, 2016. "Asymptotic refinements of nonparametric bootstrap for quasi‐likelihood ratio tests for classes of extremum estimators," Econometrics Journal, Royal Economic Society, vol. 19(1), pages 33-54, February.
    • Handle: RePEc:wly:emjrnl:v:19:y:2016:i:1:p:33-54
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/ectj.12060
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lavergne, Pascal & Bertail, Patrice, 2020. "Bootstrapping Quasi Likelihood Ratio Tests under Misspecification," TSE Working Papers 20-1102, Toulouse School of Economics (TSE).

    More about this item

    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:wly:emjrnl:v:19:y:2016:i:1:p:33-54. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/resssea.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.