IDEAS home Printed from https://ideas.repec.org/p/upf/upfgen/501.html
   My bibliography  Save this paper

Estimating learning models from experimental data

Author

Abstract

We study the statistical properties of three estimation methods for a model of learning that is often fitted to experimental data: quadratic deviation measures without unobserved heterogeneity, and maximum likelihood with and without unobserved heterogeneity. After discussing identification issues, we show that the estimators are consistent and provide their asymptotic distribution. Using Monte Carlo simulations, we show that ignoring unobserved heterogeneity can lead to seriously biased estimations in samples which have the typical length of actual experiments. Better small sample properties are obtained if unobserved heterogeneity is introduced. That is, rather than estimating the parameters for each individual, the individual parameters are considered random variables, and the distribution of those random variables is estimated.

Suggested Citation

  • Antonio Cabrales & Walter Garcia Fontes, 2000. "Estimating learning models from experimental data," Economics Working Papers 501, Department of Economics and Business, Universitat Pompeu Fabra.
  • Handle: RePEc:upf:upfgen:501
    as

    Download full text from publisher

    File URL: https://econ-papers.upf.edu/papers/501.pdf
    File Function: Whole Paper
    Download Restriction: no

    References listed on IDEAS

    as
    1. Kenneth Clark & Stephen Kay & Martin Sefton, 2001. "When are Nash equilibria self-enforcing? An experimental analysis," International Journal of Game Theory, Springer;Game Theory Society, vol. 29(4), pages 495-515.
    2. George R. Neumann & Nathan E. Savin, 2000. "Learning and Communication in Sender-Receiver Games: An Econometric Investigation," Econometric Society World Congress 2000 Contributed Papers 1852, Econometric Society.
    3. Martin Posch, 1997. "Cycling in a stochastic learning algorithm for normal form games," Journal of Evolutionary Economics, Springer, vol. 7(2), pages 193-207.
    4. Cabrales, Antonio & Garcia-Fontes, Walter & Motta, Massimo, 2000. "Risk dominance selects the leader: An experimental analysis," International Journal of Industrial Organization, Elsevier, vol. 18(1), pages 137-162, January.
    5. Borgers, Tilman & Sarin, Rajiv, 1997. "Learning Through Reinforcement and Replicator Dynamics," Journal of Economic Theory, Elsevier, vol. 77(1), pages 1-14, November.
    6. Tang, Fang-Fang, 1996. "Anticipatory Learning in Two-Person Games: An Experimental Study, Part II. Learning," Discussion Paper Serie B 363, University of Bonn, Germany.
    7. Martin Sefton, 1999. "A Model of Behavior in Coordination Game Experiments," Experimental Economics, Springer;Economic Science Association, vol. 2(2), pages 151-164, December.
    8. John G. Cross, 1973. "A Stochastic Learning Model of Economic Behavior," The Quarterly Journal of Economics, Oxford University Press, vol. 87(2), pages 239-266.
    9. Guth, Werner & Schmittberger, Rolf & Schwarze, Bernd, 1982. "An experimental analysis of ultimatum bargaining," Journal of Economic Behavior & Organization, Elsevier, vol. 3(4), pages 367-388, December.
    10. Matsui, Akihiko, 1992. "Best response dynamics and socially stable strategies," Journal of Economic Theory, Elsevier, vol. 57(2), pages 343-362, August.
    11. Andreas Blume & Douglas V. DeJong & George R. Neumann & N. E. Savin, 2002. "Learning and communication in sender-receiver games: an econometric investigation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(3), pages 225-247.
    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


    Cited by:

    1. Spiliopoulos, Leonidas, 2008. "Do repeated game players detect patterns in opponents? Revisiting the Nyarko & Schotter belief elicitation experiment," MPRA Paper 6666, University Library of Munich, Germany.
    2. Timothy C. Salmon, 2001. "An Evaluation of Econometric Models of Adaptive Learning," Econometrica, Econometric Society, vol. 69(6), pages 1597-1628, November.
    3. Olivier Armantier, 2006. "Do Wealth Differences Affect Fairness Considerations?," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 47(2), pages 391-429, May.
    4. Kovarik, Jaromir & Mengel, Friederike & Romero, José Gabriel, 2012. "Learning in Network Games," IKERLANAK Ikerlanak;2012-66, Universidad del País Vasco - Departamento de Fundamentos del Análisis Económico I.
    5. Fréchette, Guillaume R., 2009. "Learning in a multilateral bargaining experiment," Journal of Econometrics, Elsevier, vol. 153(2), pages 183-195, December.
    6. Nathaniel T Wilcox, 2003. "Heterogeneity and Learning Principles," Levine's Bibliography 666156000000000435, UCLA Department of Economics.
    7. Spiliopoulos, Leonidas, 2012. "Pattern recognition and subjective belief learning in a repeated constant-sum game," Games and Economic Behavior, Elsevier, vol. 75(2), pages 921-935.
    8. Armantier, Olivier, 2004. "Does observation influence learning?," Games and Economic Behavior, Elsevier, vol. 46(2), pages 221-239, February.

    More about this item

    Keywords

    Estimation methods; learning; unobserved heterogeneity; Leex;

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

    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:upf:upfgen:501. 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: (). General contact details of provider: http://www.econ.upf.edu/ .

    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 CitEc recognized a reference but did not link an item in RePEc 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 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.