IDEAS home Printed from https://ideas.repec.org/a/taf/lstaxx/v45y2016i11p3323-3331.html
   My bibliography  Save this article

Regression with an infinite number of observations applied to estimating the parameters of the stable distribution using the empirical characteristic function

Author

Listed:
  • J. Martin Van Zyl

Abstract

The parameters of stable law parameters can be estimated using a regression based approach involving the empirical characteristic function. One approach is to use a fixed number of points for all parameters of the distribution to estimate the characteristic function. In this work the results are derived where all points in an interval is used to estimate the empirical characteristic function, thus least squares estimators of a linear function of the parameters, using an infinite number of observations. It was found that the procedure performs very good in small samples.

Suggested Citation

  • J. Martin Van Zyl, 2016. "Regression with an infinite number of observations applied to estimating the parameters of the stable distribution using the empirical characteristic function," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 45(11), pages 3323-3331, June.
    • Handle: RePEc:taf:lstaxx:v:45:y:2016:i:11:p:3323-3331
    DOI: 10.1080/03610926.2014.901382
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03610926.2014.901382
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/03610926.2014.901382?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.

    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:taf:lstaxx:v:45:y:2016:i:11:p:3323-3331. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/lsta .

    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.