IDEAS home Printed from https://ideas.repec.org/a/taf/gnstxx/v30y2018i2p392-429.html
   My bibliography  Save this article

Estimation of the error distribution in a varying coefficient regression model

Author

Listed:
  • Anton Schick
  • Yilin Zhu
  • Xiaojie Du

Abstract

This paper deals with the estimation of the error distribution function in a varying coefficient regression model. We propose two estimators and study their asymptotic properties by obtaining uniform stochastic expansions. The first estimator is a residual-based empirical distribution function. We study this estimator when the varying coefficients are estimated by under-smoothed local quadratic smoothers. Our second estimator which exploits the fact that the error distribution has mean zero is a weighted residual-based empirical distribution whose weights are chosen to achieve the mean zero property using empirical likelihood methods. The second estimator improves on the first estimator. Bootstrap confidence bands based on the two estimators are also discussed.

Suggested Citation

  • Anton Schick & Yilin Zhu & Xiaojie Du, 2018. "Estimation of the error distribution in a varying coefficient regression model," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 30(2), pages 392-429, April.
    • Handle: RePEc:taf:gnstxx:v:30:y:2018:i:2:p:392-429
    DOI: 10.1080/10485252.2018.1429608
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/10485252.2018.1429608
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/10485252.2018.1429608?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:gnstxx:v:30:y:2018:i:2:p:392-429. 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/GNST20 .

    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.