IDEAS home Printed from https://ideas.repec.org/a/ecm/emetrp/v55y1987i4p875-91.html
   My bibliography  Save this article

Asymptotically Efficient Estimation in the Presence of Heteroskedasticity of Unknown Form

Author

Listed:
  • Robinson, P M

Abstract

In a multiple-regression model the residual variance is an unknown function of the explanatory variables, and estimated by nearest-neighbor nonparametric regression. The resulting weighted least-squares estimator of the regression coefficients is shown to be adaptive, in the sense of having the same asymptotic distribution, to first order, as estimators based on knowledge of the actual variance function or a finite parameterization of it. A similar result was established by R. J. Carrol l (1982) using kernel estimation and under substantially more restrictive conditions on the data generating process than ours. Extensions to various other models seem to be possible. Copyright 1987 by The Econometric Society.

Suggested Citation

  • Robinson, P M, 1987. "Asymptotically Efficient Estimation in the Presence of Heteroskedasticity of Unknown Form," Econometrica, Econometric Society, vol. 55(4), pages 875-891, July.
  • Handle: RePEc:ecm:emetrp:v:55:y:1987:i:4:p:875-91
    as

    Download full text from publisher

    File URL: http://links.jstor.org/sici?sici=0012-9682%28198707%2955%3A4%3C875%3AAEEITP%3E2.0.CO%3B2-A&origin=repec
    File Function: full text
    Download Restriction: Access to full text is restricted to JSTOR subscribers. See http://www.jstor.org for details.

    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:ecm:emetrp:v:55:y:1987:i:4:p:875-91. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Wiley-Blackwell Digital Licensing) or (Christopher F. Baum). General contact details of provider: http://edirc.repec.org/data/essssea.html .

    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 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.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.