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Discriminating between Preference Functionals: A Preliminary Monte Carlo Study

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  • Carbone, Enrica
  • Hey, John D

Abstract

This article is connected with recent attempts to estimate EU and Generalised EU preference functionals using (complete ranking) experimental data and maximum likelihood estimation techniques. In particular, we explore, using Monte Carlo techniques, the power of such procedures in correctly determining the true preference functional. We conclude that several of the more popular generalisations to EU are very difficult to disentangle, and that the techniques are rather poor at correctly identifying EU when it is the correct functional. Copyright 1994 by Kluwer Academic Publishers

Suggested Citation

  • Carbone, Enrica & Hey, John D, 1994. "Discriminating between Preference Functionals: A Preliminary Monte Carlo Study," Journal of Risk and Uncertainty, Springer, vol. 8(3), pages 223-242, May.
    • Handle: RePEc:kap:jrisku:v:8:y:1994:i:3:p:223-42
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    Cited by:

    1. David Bruner, 2009. "Changing the probability versus changing the reward," Experimental Economics, Springer;Economic Science Association, vol. 12(4), pages 367-385, December.
    2. John Hey & Enrica Carbone, "undated". "Which Error Theory is Best?," Discussion Papers 99/31, Department of Economics, University of York.
    3. Healy, Paul J. & Park, Hyoeun, 2023. "Model selection accuracy in behavioral game theory: A simulation," European Economic Review, Elsevier, vol. 152(C).
    4. 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.
    5. John Hey, 2018. "Comparing Theories: What Are We Looking For?," World Scientific Book Chapters, in: Experiments in Economics Decision Making and Markets, chapter 14, pages 331-352, World Scientific Publishing Co. Pte. Ltd..
    6. Bertrand Munier, 1995. "Méthodes expérimentales d'évaluation des théories du risque," Revue Économique, Programme National Persée, vol. 46(3), pages 939-949.
    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. Henry Stott, 2006. "Cumulative prospect theory's functional menagerie," Journal of Risk and Uncertainty, Springer, vol. 32(2), pages 101-130, March.
    9. David M. Bruner, 2017. "Does decision error decrease with risk aversion?," Experimental Economics, Springer;Economic Science Association, vol. 20(1), pages 259-273, March.

    More about this item

    JEL classification:

    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General

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