IDEAS home Printed from https://ideas.repec.org/a/tpr/restat/v100y2018i3p528-534.html
   My bibliography  Save this article

An Empirical Evaluation of the Toolbox Model of Lottery Choices

Author

Listed:
  • Dale O. Stahl

    (University of Texas at Austin)

Abstract

Can a toolbox of simple heuristic rules help explain lottery choices relative to expected utility theory (EUT)? While a mixture model of EUT plus heuristic rules will obviously fit data better than EUT only, given the small sample sizes, there is a danger of overfitting. Therefore, instead of goodness-of-fit measures, we focus on forecasting performance. Using two data sets of binary lottery choices and reasonable holdout subsets for testing forecasting performance, we find that the EUT-only model forecasts better than the toolbox mixture model with EUT. Even when the toolbox model with EUT fits the data significantly better, EUT-only forecasts better.

Suggested Citation

  • Dale O. Stahl, 2018. "An Empirical Evaluation of the Toolbox Model of Lottery Choices," The Review of Economics and Statistics, MIT Press, vol. 100(3), pages 528-534, July.
    • Handle: RePEc:tpr:restat:v:100:y:2018:i:3:p:528-534
    as

    Download full text from publisher

    File URL: http://www.mitpressjournals.org/doi/pdf/10.1162/rest_a_00700
    Download Restriction: Access to PDF is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

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


    Cited by:

    1. Konstantinos Georgalos & Nathan Nabil, 2023. "Heuristics Unveiled," Working Papers 400814162, Lancaster University Management School, Economics Department.
    2. Konstantinos Georgalos & Nathan Nabil, 2023. "Testing Models of Complexity Aversion," Working Papers 400814269, Lancaster University Management School, Economics Department.

    More about this item

    Statistics

    Access and download statistics

    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:tpr:restat:v:100:y:2018:i:3:p:528-534. 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: Kelly McDougall (email available below). General contact details of provider: https://direct.mit.edu/journals .

    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.