Advanced Search
MyIDEAS: Login

Consistent Estimation of Structural Parameters in Regression Models with Adaptive Learning


Author Info

  • Norbert Christopeit

    (University of Bonn)

  • Michael Massmann

    (VU University Amsterdam)

Registered author(s):


    In this paper we consider regression models with forecast feedback. Agents' expectations are formed via the recursive estimation of the parameters in an auxiliary model. The learning scheme employed by the agents belongs to the class of stochastic approximation algorithms whose gain sequence is decreasing to zero. Our focus is on the estimation of the parameters in the resulting actual law of motion. For a special case we show that the ordinary least squares estimator is consistent.

    Download Info

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
    File URL:
    Download Restriction: no

    Bibliographic Info

    Paper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 10-077/4.

    as in new window
    Date of creation: 23 Aug 2010
    Date of revision:
    Handle: RePEc:dgr:uvatin:20100077

    Contact details of provider:
    Web page:

    Related research

    Keywords: Adaptive learning; forecast feedback; stochastic approximation; linear regression with stochastic regressors; consistency;

    Find related papers by JEL classification:

    This paper has been announced in the following NEP Reports:


    No references listed on IDEAS
    You can help add them by filling out this form.



    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.


    Access and download statistics


    When requesting a correction, please mention this item's handle: RePEc:dgr:uvatin:20100077. 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: (Antoine Maartens (+31 626 - 160 892)).

    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.

    If references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link to it, you can help with 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 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.