Advanced Search
MyIDEAS: Login to save this article or follow this journal

The Principal-Agent Approach to Testing Experts

Contents:

Author Info

  • Wojciech Olszewski
  • Marcin Pęski
Registered author(s):

    Abstract

    Recent literature on testing experts shows that it is often impossible to determine whether an expert knows the stochastic process that generates data. Despite this negative result, we show that there often exist contracts that allow a decision maker to attain the first-best payoff without learning the expert's type. This kind of full-surplus extraction is always possible in infinite-horizon models in which future payoffs are not discounted. If future payoffs are discounted (but the discount factor tends to 1), the possibility of full-surplus extraction depends on a constraint involving the forecasting technology. (JEL D82)

    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: http://www.aeaweb.org/articles.php?doi=10.1257/mic.3.2.89
    Download Restriction: Access to full text is restricted to AEA members and institutional subscribers.

    As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

    Bibliographic Info

    Article provided by American Economic Association in its journal American Economic Journal: Microeconomics.

    Volume (Year): 3 (2011)
    Issue (Month): 2 (May)
    Pages: 89-113

    as in new window
    Handle: RePEc:aea:aejmic:v:3:y:2011:i:2:p:89-113

    Note: DOI: 10.1257/mic.3.2.89
    Contact details of provider:
    Email:
    Web page: https://www.aeaweb.org/aej-micro
    More information through EDIRC

    Order Information:
    Web: https://www.aeaweb.org/subscribe.html

    Related research

    Keywords:

    Find related papers by JEL classification:

    References

    References listed on IDEAS
    Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
    as in new window
    1. Vladimir Vovk & Glenn Shafer, 2005. "Good randomized sequential probability forecasting is always possible," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 747-763.
    2. Fudenberg, Drew & Levine, David, 1999. "An Easier Way to Calibrate," Scholarly Articles 3203773, Harvard University Department of Economics.
    3. Eddie Dekel & Yossi Feinberg, 2006. "Non-Bayesian Testing of a Stochastic Prediction," Discussion Papers 1418, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
    4. Alvaro Sandroni, 2003. "The reproducible properties of correct forecasts," International Journal of Game Theory, Springer, vol. 32(1), pages 151-159, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as in new window

    Cited by:
    1. Irene Valsecchi, 2013. "The expert problem: a survey," Economics of Governance, Springer, vol. 14(4), pages 303-331, November.
    2. Colin, Stewart, 2011. "Nonmanipulable Bayesian testing," Journal of Economic Theory, Elsevier, vol. 146(5), pages 2029-2041, September.
    3. Alvaro Sandroni, 2014. "At Least Do No Harm: The Use of Scarce Data," American Economic Journal: Microeconomics, American Economic Association, vol. 6(1), pages 1-3, February.

    Lists

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

    Statistics

    Access and download statistics

    Corrections

    When requesting a correction, please mention this item's handle: RePEc:aea:aejmic:v:3:y:2011:i:2:p:89-113. 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: (Jane Voros) or (Michael P. Albert).

    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.