IDEAS home Printed from https://ideas.repec.org/p/cep/stidar/19.html
   My bibliography  Save this paper

Robust Estimation of Income Distribution Models with Grouped Data

Author

Listed:
  • Elvezio Ronchetti
  • Maria-Pia Victoria-Feser

Abstract

An important aspect of income distribution is the modelling of the data using an appropriate parametric model. This involves estimating the parameters of the models, given the data at hand. Income data are typically in grouped form. Moreover, they are not always reliable in that they may contain contamination. Classical estimation procedures with grouped data are now widely available, but are typically not robust in that a small amount of contaminated data can considerably bias the estimation. In this paper we investigate the robustness properties of the class of minimum power divergence estimators for grouped data. This class contains the classical maximum likelihood estimators and other well known classical estimators. We find that the bias of these estimators due to deviations from the assumed underlying model can be large. Therefore we propsose a more general class of estimators which allow us to construct robust procedures. We define optimal bounded influence function estimators and by a simulation study, we show that under small model contaminations, they are more stable than the classical estimators for grouped data. Finally, our results are applied to a particular real example.

Suggested Citation

  • Elvezio Ronchetti & Maria-Pia Victoria-Feser, 1996. "Robust Estimation of Income Distribution Models with Grouped Data," STICERD - Distributional Analysis Research Programme Papers 19, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
  • Handle: RePEc:cep:stidar:19
    as

    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.

    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:cep:stidar:19. 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: the person in charge (email available below). General contact details of provider: https://sticerd.lse.ac.uk/_new/publications/ .

    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.