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Overconfident for real? Proper scoring for confidence intervals

Author

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  • Michał Krawczyk

    () (Faculty of Economic Sciences, University of Warsaw)

Abstract

Studies show that people tend to provide overly narrow confidence intervals for unknown values. Such a form of overconfidence would have an important impact on financial markets, among other domains, leading i.a. to excessive trading. The present study is one of the very few that try to incentivize reporting correct confidence intervals. To this end, a reward scheme is proposed, based on a combination of asymmetric loss functions minimized by appropriate quantiles of a probability distribution. In the experiment I find that incentivized subjects provide wider confidence intervals, obtaining a higher hit rate than the control group. The effect is stronger than that of feedback and explicit warning. These findings suggest that the overly narrow confidence intervals reported elsewhere are partly due to an insufficient mental effort that subjects exert and that they can be induced to do so by the proposed incentive scheme.

Suggested Citation

  • Michał Krawczyk, 2011. "Overconfident for real? Proper scoring for confidence intervals," Working Papers 2011-15, Faculty of Economic Sciences, University of Warsaw.
  • Handle: RePEc:war:wpaper:2011-15
    as

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    File URL: http://www.wne.uw.edu.pl/inf/wyd/WP/WNE_WP55.pdf
    File Function: First version, 2011
    Download Restriction: no

    References listed on IDEAS

    as
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    3. Itzhak Ben-David & John R. Graham, 2013. "Managerial Miscalibration," The Quarterly Journal of Economics, Oxford University Press, vol. 128(4), pages 1547-1584.
    4. Bruno Biais & Denis Hilton & Karine Mazurier & Sébastien Pouget, 2005. "Judgemental Overconfidence, Self-Monitoring, and Trading Performance in an Experimental Financial Market," Review of Economic Studies, Oxford University Press, vol. 72(2), pages 287-312.
    5. Karl Schlag & Joël van der Weele, 2009. "Efficient interval scoring rules," Economics Working Papers 1176, Department of Economics and Business, Universitat Pompeu Fabra.
    6. Urs Fischbacher, 2007. "z-Tree: Zurich toolbox for ready-made economic experiments," Experimental Economics, Springer;Economic Science Association, vol. 10(2), pages 171-178, June.
    7. Murphy, James J. & Stevens, Thomas H., 2004. "Contingent Valuation, Hypothetical Bias, and Experimental Economics," Agricultural and Resource Economics Review, Cambridge University Press, vol. 33(02), pages 182-192, October.
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    Citations

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    Cited by:

    1. Karl H. Schlag & Joël J. van der Weele, 2015. "A method to elicit beliefs as most likely intervals," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 10(5), pages 456-468, September.
    2. Karl Schlag & James Tremewan & Joël Weele, 2015. "A penny for your thoughts: a survey of methods for eliciting beliefs," Experimental Economics, Springer;Economic Science Association, vol. 18(3), pages 457-490, September.

    More about this item

    Keywords

    overconfidence; calibration; confidence intervals; proper scoring rules;

    JEL classification:

    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • D03 - Microeconomics - - General - - - Behavioral Microeconomics: Underlying Principles
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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