IDEAS home Printed from
   My bibliography  Save this paper

Kernel Estimation of Average Derivatives and Differences


  • Mark Coppejans
  • Holger Sieg


In this paper, we derive nonparametric average difference estimators. We show that this estimator is consistent and root-$N$ asymptotically normally distributed. Furthermore, the average difference estimator converges to the well-known average derivative estimator as the increment used to compute the difference converges to zero. We apply this estimator to test for differences between average and marginal compensation of workers. We estimate different versions of the model using repeated cross-sectional data from the CPS for a number of narrowly defined occupations. The average difference estimator yields plausible estimates for the average marginal compensation in all subsamples of the CPS considered in this paper. Our results highlight the importance of choosing bandwidth parameters in nonparametric estimation. If important covariates are measured discretely, standard approaches for choosing optimal bandwidth parameters do not necessarily apply. Our main empirical findings suggest that, at least for the preferred range of bandwidth parameters, marginal compensation exceeds average compensation, which suggests that average compensation is at best a noisy measure for the unobserved productivity of workers.

Suggested Citation

  • Mark Coppejans & Holger Sieg, "undated". "Kernel Estimation of Average Derivatives and Differences," GSIA Working Papers 2003-03, Carnegie Mellon University, Tepper School of Business.
  • Handle: RePEc:cmu:gsiawp:1909861039

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    More about this item

    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:cmu:gsiawp:1909861039. 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: (Steve Spear). 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.