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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.

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Bibliographic Info

Article provided by American Statistical Association in its journal Journal of Business and Economic Statistics.

Volume (Year): 22 (2004)
Issue (Month): 1 (January)
Pages: 64-79

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Handle: RePEc:bes:jnlbes:v:22:y:2004:i:1:p:64-79

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  4. Ellison, Glenn & Fudenberg, Drew, 1992. "Rules of Thumb for Social Learning," IDEI Working Papers 17, Institut d'Économie Industrielle (IDEI), Toulouse.
  5. Harald Uhlig & Martin Lettau, 1999. "Rules of Thumb versus Dynamic Programming," American Economic Review, American Economic Association, vol. 89(1), pages 148-174, March.
  6. Pierre-Olivier Gourinchas & Jonathan A. Parker, 1999. "Consumption Over the Life Cycle," NBER Working Papers 7271, National Bureau of Economic Research, Inc.
  7. Anderlini, Luca & Canning, David, 2001. "Structural Stability Implies Robustness to Bounded Rationality," Journal of Economic Theory, Elsevier, vol. 101(2), pages 395-422, December.
  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.
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Cited by:
  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. Schunk, Daniel & Winter, Joachim, 2007. "The Relationship Between Risk Attitudes and Heuristics in Search Tasks: A Laboratory Experiment," Discussion Papers in Economics 1377, University of Munich, Department of Economics.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. Li Hao & Daniel Houser, 2013. "Perceptions, Intentions, and Cheating," Working Papers 1039, George Mason University, Interdisciplinary Center for Economic Science, revised Feb 2013.

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