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Mixture Models of Choice Under Risk

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Author Info
Anna Conte
John D Hey
Peter G Moffatt

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Abstract

This paper is concerned with estimating preference functionals for choice under risk from the choice behaviour of individuals. We start from the observation that there is heterogeneity in behaviour between individuals and within individuals. By ‘heterogeneity between individuals’ we mean that people are different, not only in terms of which type of preference functional that they have, but also in terms of their parameters for these functionals. By ‘heterogeneity within individuals’ we mean that behaviour may be different even by the same individual for the same choice problem. Given the heterogeneity between individuals, the assumption of a ‘representative agent’ preference functional to represent the preference functional of all individuals may well lead to biased estimates. Given the heterogeneity within individuals, we should think carefully about the source of this heterogeneity and model it appropriately, for otherwise we get biased estimates. We propose solutions to both of these problems, concentrating particularly, but not exclusively, on using a Mixture Model to capture the heterogeneity of preference functionals across individuals.

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Paper provided by Department of Economics, University of York in its series Discussion Papers with number 07/06.

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Date of creation: Apr 2007
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Handle: RePEc:yor:yorken:07/06

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Related research
Keywords: errors expected utility theory experimental economics maximum simulated likelihood mixture models preference functionals risky choice rank dependent expected utility theory unobserved heterogeneity

Find related papers by JEL classification:
C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Statistical Simulation Methods
C29 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Other
C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software
C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Nathaniel T Wilcox, 2006. "Theories of Learning in Games and Heterogeneity Bias," Econometrica, Econometric Society, vol. 74(5), pages 1271-1292, 09. [Downloadable!] (restricted)
  2. Tversky, Amos & Kahneman, Daniel, 1992. " Advances in Prospect Theory: Cumulative Representation of Uncertainty," Journal of Risk and Uncertainty, Springer, vol. 5(4), pages 297-323, October.
  3. Hey, John D & Orme, Chris, 1994. "Investigating Generalizations of Expected Utility Theory Using Experimental Data," Econometrica, Econometric Society, vol. 62(6), pages 1291-1326, November. [Downloadable!] (restricted)
  4. Hey, John D., 1995. "Experimental investigations of errors in decision making under risk," European Economic Review, Elsevier, vol. 39(3-4), pages 633-640, April. [Downloadable!] (restricted)
  5. John Hey, 2001. "Does Repetition Improve Consistency?," Experimental Economics, Springer, vol. 4(1), pages 5-54, June. [Downloadable!] (restricted)
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  6. Peter Moffatt, 2005. "Stochastic Choice and the Allocation of Cognitive Effort," Experimental Economics, Springer, vol. 8(4), pages 369-388, December. [Downloadable!] (restricted)
  7. Peter Moffatt & Simon Peters, 2001. "Testing for the Presence of a Tremble in Economic Experiments," Experimental Economics, Springer, vol. 4(3), pages 221-228, December. [Downloadable!] (restricted)
  8. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-33, March. [Downloadable!] (restricted)
  9. Chris Starmer, 2000. "Developments in Non-expected Utility Theory: The Hunt for a Descriptive Theory of Choice under Risk," Journal of Economic Literature, American Economic Association, vol. 38(2), pages 332-382, June. [Downloadable!] (restricted)
  10. Quiggin, John, 1982. "A theory of anticipated utility," Journal of Economic Behavior & Organization, Elsevier, vol. 3(4), pages 323-343, December. [Downloadable!] (restricted)
  11. Kenneth Train, 2003. "Discrete Choice Methods with Simulation," Online economics textbooks, SUNY-Oswego, Department of Economics, number emetr2, September. [Downloadable!]
  12. David Buschena & David Zilberman, 2000. "Generalized Expected Utility, Heteroscedastic Error, and Path Dependence in Risky Choice," Journal of Risk and Uncertainty, Springer, vol. 20(1), pages 67-88, January. [Downloadable!] (restricted)
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Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Fabrizio Botti & Anna Conte & Daniela Di Cagno & Carlo D'Ippoliti, 2008. "Risk Attitude in Real Decision Problems," Advances in Economic Analysis & Policy, Berkeley Electronic Press, vol. 8(1), pages 1798-1798. [Downloadable!] (restricted)
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