IDEAS home Printed from https://ideas.repec.org/a/bla/jorssb/v66y2004i1p63-78.html
   My bibliography  Save this article

Estimation and testing stationarity for double‐autoregressive models

Author

Listed:
  • Shiqing Ling

Abstract

Summary. The paper considers the double‐autoregressive model yt = φyt−1+ɛt with ɛt =. Consistency and asymptotic normality of the estimated parameters are proved under the condition E ln |φ +√αηt| 1 as well as . It is well known that all kinds of estimators of φ in these cases are not normal when ɛt are independent and identically distributed. Our result is novel and surprising. Two tests are proposed for testing stationarity of the model and their asymptotic distributions are shown to be a function of bivariate Brownian motions. Critical values of the tests are tabulated and some simulation results are reported. An application to the US 90‐day treasury bill rate series is given.

Suggested Citation

  • Shiqing Ling, 2004. "Estimation and testing stationarity for double‐autoregressive models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(1), pages 63-78, February.
    • Handle: RePEc:bla:jorssb:v:66:y:2004:i:1:p:63-78
    DOI: 10.1111/j.1467-9868.2004.00432.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1467-9868.2004.00432.x
    Download Restriction: no
    File URL: https://libkey.io/10.1111/j.1467-9868.2004.00432.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:jorssb:v:66:y:2004:i:1:p:63-78. 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/rssssea.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.