IDEAS home Printed from https://ideas.repec.org/p/yor/yorken/07-06.html
   My bibliography  Save this paper

Mixture Models of Choice Under Risk

Author

Listed:
  • Anna Conte
  • John D Hey
  • Peter G Moffatt

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.

Suggested Citation

  • Anna Conte & John D Hey & Peter G Moffatt, 2007. "Mixture Models of Choice Under Risk," Discussion Papers 07/06, Department of Economics, University of York.
  • Handle: RePEc:yor:yorken:07/06
    as

    Download full text from publisher

    File URL: https://www.york.ac.uk/media/economics/documents/discussionpapers/2007/0706.pdf
    File Function: Main text
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Loomes, Graham & Moffatt, Peter G & Sugden, Robert, 2002. "A Microeconometric Test of Alternative Stochastic Theories of Risky Choice," Journal of Risk and Uncertainty, Springer, vol. 24(2), pages 103-130, March.
    2. Loomes, Graham & Sugden, Robert, 1998. "Testing Different Stochastic Specifications of Risky Choice," Economica, London School of Economics and Political Science, vol. 65(260), pages 581-598, November.
    3. Kreps,David M. & Wallis,Kenneth F. (ed.), 1997. "Advances in Economics and Econometrics: Theory and Applications," Cambridge Books, Cambridge University Press, number 9780521589833.
    4. Glenn Harrison & E. Rutström, 2009. "Expected utility theory and prospect theory: one wedding and a decent funeral," Experimental Economics, Springer;Economic Science Association, vol. 12(2), pages 133-158, June.
    5. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, November.
    6. David Buschena & David Zilberman, 2008. "Generalized expected utility, heteroscedastic error, and path dependence in risky choice," Journal of Risk and Uncertainty, Springer, vol. 36(2), pages 201-201, April.
    7. John D. Hey, 2018. "Does Repetition Improve Consistency?," World Scientific Book Chapters, in: Experiments in Economics Decision Making and Markets, chapter 2, pages 13-62, World Scientific Publishing Co. Pte. Ltd..
    8. John D. Hey (ed.), 1997. "The Economics of Uncertainty," Books, Edward Elgar Publishing, volume 0, number 912.
    9. 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.
    10. Kreps,David M. & Wallis,Kenneth F. (ed.), 1997. "Advances in Economics and Econometrics: Theory and Applications," Cambridge Books, Cambridge University Press, number 9780521589819.
    11. John D. Hey & Chris Orme, 2018. "Investigating Generalizations Of Expected Utility Theory Using Experimental Data," World Scientific Book Chapters, in: Experiments in Economics Decision Making and Markets, chapter 3, pages 63-98, World Scientific Publishing Co. Pte. Ltd..
    12. Peter Moffatt, 2005. "Stochastic Choice and the Allocation of Cognitive Effort," Experimental Economics, Springer;Economic Science Association, vol. 8(4), pages 369-388, December.
    13. Peter P. Wakker, 2008. "Explaining the characteristics of the power (CRRA) utility family," Health Economics, John Wiley & Sons, Ltd., vol. 17(12), pages 1329-1344.
    14. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-333, March.
    15. Pavlo R. Blavatskyy, "undated". "A Stochastic Expected Utility Theory," IEW - Working Papers 231, Institute for Empirical Research in Economics - University of Zurich.
    16. Peter P. Wakker, 2008. "Explaining the characteristics of the power (CRRA) utility family," Health Economics, John Wiley & Sons, Ltd., vol. 17(12), pages 1329-1344, December.
    17. John D. Hey, 2018. "Experimental investigations of errors in decision making under risk," World Scientific Book Chapters, in: Experiments in Economics Decision Making and Markets, chapter 17, pages 381-388, World Scientific Publishing Co. Pte. Ltd..
    18. Peter Moffatt & Simon Peters, 2001. "Testing for the Presence of a Tremble in Economic Experiments," Experimental Economics, Springer;Economic Science Association, vol. 4(3), pages 221-228, December.
    19. Henry Stott, 2006. "Cumulative prospect theory's functional menagerie," Journal of Risk and Uncertainty, Springer, vol. 32(2), pages 101-130, March.
    20. Quiggin, John, 1982. "A theory of anticipated utility," Journal of Economic Behavior & Organization, Elsevier, vol. 3(4), pages 323-343, December.
    21. Botti Fabrizio & Conte Anna & Di Cagno Daniela Teresa & D'Ippoliti Carlo, 2008. "Risk Attitude in Real Decision Problems," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 8(1), pages 1-32, March.
    22. Harless, David W & Camerer, Colin F, 1994. "The Predictive Utility of Generalized Expected Utility Theories," Econometrica, Econometric Society, vol. 62(6), pages 1251-1289, November.
    23. Nathaniel T Wilcox, 2006. "Theories of Learning in Games and Heterogeneity Bias," Econometrica, Econometric Society, vol. 74(5), pages 1271-1292, September.
    24. Kreps,David M. & Wallis,Kenneth F. (ed.), 1997. "Advances in Economics and Econometrics: Theory and Applications," Cambridge Books, Cambridge University Press, number 9780521589826.
    25. Pavlo Blavatskyy, 2007. "Stochastic expected utility theory," Journal of Risk and Uncertainty, Springer, vol. 34(3), pages 259-286, June.
    26. Ballinger, T Parker & Wilcox, Nathaniel T, 1997. "Decisions, Error and Heterogeneity," Economic Journal, Royal Economic Society, vol. 107(443), pages 1090-1105, July.
    27. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wilcox, Nathaniel T., 2011. "'Stochastically more risk averse:' A contextual theory of stochastic discrete choice under risk," Journal of Econometrics, Elsevier, vol. 162(1), pages 89-104, May.
    2. Jakusch, Sven Thorsten, 2017. "On the applicability of maximum likelihood methods: From experimental to financial data," SAFE Working Paper Series 148, Leibniz Institute for Financial Research SAFE, revised 2017.
    3. Andersen, Steffen & Harrison, Glenn W. & Lau, Morten Igel & Rutström, Elisabet E., 2010. "Behavioral econometrics for psychologists," Journal of Economic Psychology, Elsevier, vol. 31(4), pages 553-576, August.
    4. Andreas C Drichoutis & Jayson L Lusk, 2014. "Judging Statistical Models of Individual Decision Making under Risk Using In- and Out-of-Sample Criteria," PLOS ONE, Public Library of Science, vol. 9(7), pages 1-13, July.
    5. John Hey & Andrea Morone & Ulrich Schmidt, 2009. "Noise and bias in eliciting preferences," Journal of Risk and Uncertainty, Springer, vol. 39(3), pages 213-235, December.
    6. Pavlo Blavatskyy, 2007. "Stochastic expected utility theory," Journal of Risk and Uncertainty, Springer, vol. 34(3), pages 259-286, June.
    7. Jakusch, Sven Thorsten & Meyer, Steffen & Hackethal, Andreas, 2019. "Taming models of prospect theory in the wild? Estimation of Vlcek and Hens (2011)," SAFE Working Paper Series 146, Leibniz Institute for Financial Research SAFE, revised 2019.
    8. Daniel Navarro-Martinez & Graham Loomes & Andrea Isoni & David Butler & Larbi Alaoui, 2018. "Boundedly rational expected utility theory," Journal of Risk and Uncertainty, Springer, vol. 57(3), pages 199-223, December.
    9. Adam Booij & Bernard Praag & Gijs Kuilen, 2010. "A parametric analysis of prospect theory’s functionals for the general population," Theory and Decision, Springer, vol. 68(1), pages 115-148, February.
    10. Michael H. Birnbaum & Ulrich Schmidt & Miriam D. Schneider, 2017. "Testing independence conditions in the presence of errors and splitting effects," Journal of Risk and Uncertainty, Springer, vol. 54(1), pages 61-85, February.
    11. Pavlo R. Blavatskyy & Ganna Pogrebna, 2010. "Models of stochastic choice and decision theories: why both are important for analyzing decisions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(6), pages 963-986.
    12. Pavlo Blavatskyy, 2014. "Stronger utility," Theory and Decision, Springer, vol. 76(2), pages 265-286, February.
    13. Henry Stott, 2006. "Cumulative prospect theory's functional menagerie," Journal of Risk and Uncertainty, Springer, vol. 32(2), pages 101-130, March.
    14. David M. Bruner, 2017. "Does decision error decrease with risk aversion?," Experimental Economics, Springer;Economic Science Association, vol. 20(1), pages 259-273, March.
    15. Blavatskyy, Pavlo, 2016. "Probability weighting and L-moments," European Journal of Operational Research, Elsevier, vol. 255(1), pages 103-109.
    16. Nathaniel T. Wilcox, 2015. "Error and Generalization in Discrete Choice Under Risk," Working Papers 15-11, Chapman University, Economic Science Institute.
    17. Levon Barseghyan & Francesca Molinari & Ted O'Donoghue & Joshua C. Teitelbaum, 2013. "The Nature of Risk Preferences: Evidence from Insurance Choices," American Economic Review, American Economic Association, vol. 103(6), pages 2499-2529, October.
    18. Pavlo Blavatskyy, 2018. "A second-generation disappointment aversion theory of decision making under risk," Theory and Decision, Springer, vol. 84(1), pages 29-60, January.
    19. John D. Hey, 2018. "Why We Should Not Be Silent About Noise," World Scientific Book Chapters, in: Experiments in Economics Decision Making and Markets, chapter 13, pages 309-329, World Scientific Publishing Co. Pte. Ltd..
    20. Aurora García-Gallego & Nikolaos Georgantzís & Daniel Navarro-Martínez & Gerardo Sabater-Grande, 2011. "The stochastic component in choice and regression to the mean," Theory and Decision, Springer, vol. 71(2), pages 251-267, August.

    More about this item

    Keywords

    errors; expected utility theory; experimental economics; maximum simulated likelihood; mixture models; preference functionals; risky choice; rank dependent expected utility theory; unobserved heterogeneity;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • 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

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:yor:yorken:07/06. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Paul Hodgson (email available below). General contact details of provider: https://edirc.repec.org/data/deyoruk.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.