IDEAS home Printed from https://ideas.repec.org/a/bla/jtsera/v29y2008i3p513-532.html
   My bibliography  Save this article

Improved inference for first‐order autocorrelation using likelihood analysis

Author

Listed:
  • M. Rekkas
  • Y. Sun
  • A. Wong

Abstract

. Testing for first‐order autocorrelation in small samples using the standard asymptotic test can be seriously misleading. Recent methods in likelihood asymptotics are used to derive more accurate p‐value approximations for testing the autocorrelation parameter in a regression model. The methods are based on conditional evaluations and are thus specific to the particular data obtained. A numerical example and three simulations are provided to show that this new likelihood method provides higher order improvements and is superior in terms of central coverage even for autocorrelation parameter values close to unity.

Suggested Citation

  • M. Rekkas & Y. Sun & A. Wong, 2008. "Improved inference for first‐order autocorrelation using likelihood analysis," Journal of Time Series Analysis, Wiley Blackwell, vol. 29(3), pages 513-532, May.
    • Handle: RePEc:bla:jtsera:v:29:y:2008:i:3:p:513-532
    DOI: 10.1111/j.1467-9892.2007.00567.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1467-9892.2007.00567.x
    Download Restriction: no
    File URL: https://libkey.io/10.1111/j.1467-9892.2007.00567.x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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:bla:jtsera:v:29:y:2008:i:3:p:513-532. 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: http://www.blackwellpublishing.com/journal.asp?ref=0143-9782 .

    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.