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How Do Behavioral Assumptions Affect Structural Inference? Evidence from a Laboratory Experiment

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  • Houser, Daniel
  • Winter, Joachim

Abstract

We use laboratory experiments to investigate the effect that assuming rational expectations has on structural inference in a dynamic discrete decision problem. Our design induces preferences up to the subjective rate of time preference, leaving unrestricted both this parameter and subjects’ decision rules. We estimate subjects’ discount rates under the assumption that all subjects use the rational expectations decision rule, and under weaker behavioral assumptions that allow decision rule heterogeneity. We find that certain sophisticated heuristics fit subjects’ decisions statistically significantly better than rational expectations. However, the rational expectations assumption does not distort inferences about the cross-sectional discount rate distribution.

Suggested Citation

  • Houser, Daniel & Winter, Joachim, 2004. "How Do Behavioral Assumptions Affect Structural Inference? Evidence from a Laboratory Experiment," Munich Reprints in Economics 19372, University of Munich, Department of Economics.
  • Handle: RePEc:lmu:muenar:19372
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    References listed on IDEAS

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    1. El-Gamal, Mahmoud A. & Grether, David M., 1995. "Are People Bayesian? Uncovering Behavioral Strategies," Working Papers 919, California Institute of Technology, Division of the Humanities and Social Sciences.
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    Citations

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    Cited by:

    1. Schunk, Daniel & Winter, Joachim, 2009. "The relationship between risk attitudes and heuristics in search tasks: A laboratory experiment," Journal of Economic Behavior & Organization, Elsevier, vol. 71(2), pages 347-360, August.
    2. Tetsuo Yamamori & Kazuyuki IwataAuthor-Name: Akira Ogawa, 2014. "An Experimental Study of Money Illusion in Intertemporal Decision Making," Working Papers e85, Tokyo Center for Economic Research.
    3. Daniel Houser & Michael Keane & Kevin McCabe, 2004. "Behavior in a Dynamic Decision Problem: An Analysis of Experimental Evidence Using a Bayesian Type Classification Algorithm," Econometrica, Econometric Society, vol. 72(3), pages 781-822, May.
    4. Franz Rothlauf & Daniel Schunk & Jella Pfeiffer, 2005. "Classification of Human Decision Behavior: Finding Modular Decision Rules with Genetic Algorithms," MEA discussion paper series 05079, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
    5. Baohong Sun, 2006. "—Dynamic Structural Consumer Models and Current Marketing Issues," Marketing Science, INFORMS, vol. 25(6), pages 625-628, 11-12.
    6. Daniel Houser & Robert Kurzban, 2003. "Conditional cooperation and group dynamics: Experimental evidence from a sequential public goods game," Experimental 0307001, University Library of Munich, Germany, revised 21 Jan 2005.
    7. Gerald Häubl & Benedict G. C. Dellaert & Bas Donkers, 2010. "Tunnel Vision: Local Behavioral Influences on Consumer Decisions in Product Search," Marketing Science, INFORMS, vol. 29(3), pages 438-455, 05-06.
    8. Luís Santos-Pinto & Adrian Bruhin & José Mata & Thomas Åstebro, 2015. "Detecting heterogeneous risk attitudes with mixed gambles," Theory and Decision, Springer, vol. 79(4), pages 573-600, December.
    9. Schunk, Daniel, 2005. "Search behaviour with reference point preferences : theory and experimental evidence," Papers 05-12, Sonderforschungsbreich 504.
    10. Botao Yang & Andrew T. Ching, 2014. "Dynamics of Consumer Adoption of Financial Innovation: The Case of ATM Cards," Management Science, INFORMS, vol. 60(4), pages 903-922, April.
    11. Daniel Houser & Kevin McCabe & Michael Keane & Antoine Bechara, 2003. "Heuristics Used By Humans With Prefrontal Cortex Damage: Toward An Empirical Model Of Phineas Gage," Experimental 0308002, University Library of Munich, Germany.
    12. Schunk, Daniel, 2009. "Behavioral heterogeneity in dynamic search situations: Theory and experimental evidence," Journal of Economic Dynamics and Control, Elsevier, vol. 33(9), pages 1719-1738, September.
    13. Hao, Li & Houser, Daniel, 2017. "Perceptions, intentions, and cheating," Journal of Economic Behavior & Organization, Elsevier, vol. 133(C), pages 52-73.
    14. Gunnthorsdottir, Anna & Houser, Daniel & McCabe, Kevin, 2007. "Disposition, history and contributions in public goods experiments," Journal of Economic Behavior & Organization, Elsevier, vol. 62(2), pages 304-315, February.
    15. David Zetland, 2013. "Water managers are selfish like us," Chapters,in: Handbook on Experimental Economics and the Environment, chapter 14, pages 407-433 Edward Elgar Publishing.
    16. Houser, Daniel & Bechara, Antoine & Keane, Michael & McCabe, Kevin & Smith, Vernon, 2005. "Identifying individual differences: An algorithm with application to Phineas Gage," Games and Economic Behavior, Elsevier, vol. 52(2), pages 373-385, August.
    17. Dina Tasneem & Jim Engle-Warnick, 2018. "Decision Rules for Precautionary and Retirement Savings," CIRANO Working Papers 2018s-22, CIRANO.
    18. repec:eee:jeborg:v:145:y:2018:i:c:p:465-473 is not listed on IDEAS
    19. David Zetland & Marina Della Giusta, 2011. "Focal Points, Gender Norms and Reciprocation in Public Good Games," Economics & Management Discussion Papers em-dp2011-01, Henley Business School, Reading University.

    More about this item

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D90 - Microeconomics - - Micro-Based Behavioral Economics - - - General
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory

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