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How Do Behavioral Assumptions Affect Structural Inference? Evidence From A Laboratory Experiment

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  • Daniel Houser
  • Joachim Winter

    ()
    (Munich Center for the Economics of Aging (MEA))

Abstract

We use a laboratory experiment to investigate the effect that assuming rational expectations has on structural inference in a dynamic discrete choice decision problem. Our experimental design induces preferences up to each subject’s subjective rates of time preference, leaving unrestricted only this parameter and the decision rule that the subject uses in solving the problem. We analyze the data under the assumption that all subjects use the rational expectations decision rule, and also under weaker behavioral assumptions that allow for heterogeneity in the way people form decisions. We find no evidence that assuming rational expectations distorts inferences about the cross-sectional distribution of discount rates.

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Bibliographic Info

Paper provided by Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy in its series MEA discussion paper series with number 02005.

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Date of creation: 16 Jan 2002
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Handle: RePEc:mea:meawpa:02005

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References

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  1. Anderlini, Luca & Canning, David, 2001. "Structural Stability Implies Robustness to Bounded Rationality," Journal of Economic Theory, Elsevier, vol. 101(2), pages 395-422, December.
  2. repec:att:wimass:9722 is not listed on IDEAS
  3. Ellison, Glenn & Fudenberg, Drew, 1993. "Rules of Thumb for Social Learning," Scholarly Articles 3196332, Harvard University Department of Economics.
  4. 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.
  5. Hotz, V Joseph & Miller, Robert A, 1988. "An Empirical Analysis of Life Cycle Fertility and Female Labor Supply," Econometrica, Econometric Society, vol. 56(1), pages 91-118, January.
  6. Pierre-Olivier Gourinchas & Jonathan A. Parker, 2002. "Consumption Over the Life Cycle," Econometrica, Econometric Society, vol. 70(1), pages 47-89, January.
  7. Hey, John D., 1981. "Are optimal search rules reasonable? and vice versa? (And does it matter anyway?)," Journal of Economic Behavior & Organization, Elsevier, vol. 2(1), pages 47-70, March.
  8. Cox, James C & Oaxaca, Ronald L, 1999. "Can Supply and Demand Parameters Be Recovered from Data Generated by Market Institutions?," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(3), pages 285-97, July.
  9. Harald Uhlig & Martin Lettau, 1999. "Rules of Thumb versus Dynamic Programming," American Economic Review, American Economic Association, vol. 89(1), pages 148-174, March.
  10. Saul Pleeter & John T. Warner, 2001. "The Personal Discount Rate: Evidence from Military Downsizing Programs," American Economic Review, American Economic Association, vol. 91(1), pages 33-53, March.
  11. Krusell, Per & Smith, Anthony Jr., 1996. "Rules of thumb in macroeconomic equilibrium A quantitative analysis," Journal of Economic Dynamics and Control, Elsevier, vol. 20(4), pages 527-558, April.
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Citations

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Cited by:
  1. 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, 05.
  2. Schunk, Daniel & Winter, Joachim, 2004. "The Relationship Between Risk Attitudes and Heuristics in Search Tasks: A Laboratory Experiment," Sonderforschungsbereich 504 Publications 04-23, Sonderforschungsbereich 504, Universität Mannheim & Sonderforschungsbereich 504, University of Mannheim.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. Daniel Houser & Robert Kurzban, 2003. "Conditional cooperation and group dynamics: Experimental evidence from a sequential public goods game," Experimental 0307001, EconWPA, revised 21 Jan 2005.
  8. 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.
  9. Li Hao & Daniel Houser, 2013. "Perceptions, Intentions, and Cheating," Working Papers 1039, George Mason University, Interdisciplinary Center for Economic Science, revised Feb 2013.
  10. 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, EconWPA.

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