IDEAS home Printed from https://ideas.repec.org/a/spr/stmapp/v12y2004i3d10.1007_s10260-003-0064-0.html
   My bibliography  Save this article

Maximum likelihood estimation of ARFIMA models with a Box-Cox transformation

Author

Listed:
  • Angela D’Elia

    (Universitá degli Studi di Napoli Federico II)

  • Domenico Piccolo

    (Universitá degli Studi di Napoli Federico II)

Abstract

. In this paper we study the interaction between the estimation of the fractional differencing parameter d of ARFIMA models and the common practice of instantaneous transformation of the observed time series. At this aim, we first discuss the effect of a nonlinear transformation of the data on the identification of the process and on the estimate of d. Thus, we propose a joint estimation of the Box-Cox parameter and d by means of a modified normalized version of the Whittle likelihood. Then, the variance and covariance matrix of the parameters estimates is obtained. Finally, a Monte Carlo study is performed in order to check the behaviour of the proposed estimators in finite samples.

Suggested Citation

  • Angela D’Elia & Domenico Piccolo, 2004. "Maximum likelihood estimation of ARFIMA models with a Box-Cox transformation," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 12(3), pages 259-275, February.
    • Handle: RePEc:spr:stmapp:v:12:y:2004:i:3:d:10.1007_s10260-003-0064-0
    DOI: 10.1007/s10260-003-0064-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10260-003-0064-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10260-003-0064-0?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
    ---><---

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

    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:spr:stmapp:v:12:y:2004:i:3:d:10.1007_s10260-003-0064-0. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.