IDEAS home Printed from
   My bibliography  Save this paper

Parametrically and Semiparametrically Efficient Detection of Random Regression Coefficients


  • Mohamed Fihri
  • Abdelhadi Akharif
  • Amal Mellouk
  • Marc Hallin


Locally asymptotically optimal (in the Hajek-Le Cam sense) pseudo-Gaussian and rank-based procedures for detecting randomness of coefficients in linear regression models are proposed. The parametric and semiparametric efficiency properties of those procedures are investigated. Their asymptotic relative efficiencies (with respect to the pseudo-Gaussian procedure) turns out to be be huge under heavy-tailed and skewed densities, stressing the importance of an adequate choice of scores. Simulations demonstrate the excellent finite-sample performances of a class of rank-based procedures based on data-driven scores.

Suggested Citation

  • Mohamed Fihri & Abdelhadi Akharif & Amal Mellouk & Marc Hallin, 2017. "Parametrically and Semiparametrically Efficient Detection of Random Regression Coefficients," Working Papers ECARES ECARES 2017-14, ULB -- Universite Libre de Bruxelles.
  • Handle: RePEc:eca:wpaper:2013/249915

    Download full text from publisher

    File URL:
    File Function: Full text for the whole work, or for a work part
    Download Restriction: no

    More about this item


    local Asymptotic normality; optimal tests; pseudo-gaussian test; semiparametric efficiency; rank tests; random coefficient regression model;

    NEP fields

    This paper has been announced in the following NEP Reports:


    Access and download statistics


    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:eca:wpaper:2013/249915. 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: (Benoit Pauwels). General contact details of provider: .

    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.