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Parametric preference functionals under risk in the gain domain: A Bayesian analysis

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  • Kelvin Balcombe
  • Iain Fraser

Abstract

The performance of rank dependent preference functionals under risk is comprehensively evaluated using Bayesian model averaging. Model comparisons are made at three levels of heterogeneity plus three ways of linking deterministic and stochastic models: differences in utilities, differences in certainty equivalents and contextual utility. Overall, the “best model”, which is conditional on the form of heterogeneity, is a form of Rank Dependent Utility or Prospect Theory that captures most behaviour at the representative agent and individual level. However, the curvature of the probability weighting function for many individuals is S-shaped, or ostensibly concave or convex rather than the inverse S-shape commonly employed. Also contextual utility is broadly supported across all levels of heterogeneity. Finally, the Priority Heuristic model is estimated within a stochastic framework, and allowing for endogenous thresholds does improve model performance although it does not compete well with the other specifications considered. Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • Kelvin Balcombe & Iain Fraser, 2015. "Parametric preference functionals under risk in the gain domain: A Bayesian analysis," Journal of Risk and Uncertainty, Springer, vol. 50(2), pages 161-187, April.
  • Handle: RePEc:kap:jrisku:v:50:y:2015:i:2:p:161-187
    DOI: 10.1007/s11166-015-9213-8
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    3. Toritseju Begho & Kelvin Balcombe, 2023. "Attitudes to Risk and Uncertainty: New Insights From an Experiment Using Interval Prospects," SAGE Open, , vol. 13(3), pages 21582440231, July.
    4. Maren Baars & Michael Goedde‐Menke, 2022. "Ignorance illusion in decisions under risk: The impact of perceived expertise on probability weighting," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 89(1), pages 35-62, March.
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    6. Konstantinos Georgalos & Nathan Nabil, 2023. "Heuristics Unveiled," Working Papers 400814162, Lancaster University Management School, Economics Department.
    7. Galizzi, Matteo M. & Machado, Sara R. & Miniaci, Raffaele, 2016. "Temporal stability, cross-validity, and external validity of risk preferences measures: experimental evidence from a UK representative sample," LSE Research Online Documents on Economics 67554, London School of Economics and Political Science, LSE Library.
    8. Zhihua Li & Julia Müller & Peter P. Wakker & Tong V. Wang, 2018. "The Rich Domain of Ambiguity Explored," Management Science, INFORMS, vol. 64(7), pages 3227-3240, July.
    9. Li, Baibing & Hensher, David A., 2017. "Risky weighting in discrete choice," Transportation Research Part B: Methodological, Elsevier, vol. 102(C), pages 1-21.
    10. Mehmet Kutluay & Roy Brouwer & Richard S. J. Tol, 2017. "Preference updating in public health risk valuation," Working Paper Series 1517, Department of Economics, University of Sussex Business School.
    11. Ilke Aydogan & Yu Gao, 2020. "Experience and rationality under risk: re-examining the impact of sampling experience," Experimental Economics, Springer;Economic Science Association, vol. 23(4), pages 1100-1128, December.
    12. Monica Novackova & Richard S.J. Tol, 2018. "Climate Change Awareness and Willingness to Pay for its Mitigation: Evidence from the UK," Working Paper Series 0318, Department of Economics, University of Sussex Business School.
    13. Alam, Jessica & Georgalos, Konstantinos & Rolls, Harrison, 2022. "Risk preferences, gender effects and Bayesian econometrics," Journal of Economic Behavior & Organization, Elsevier, vol. 202(C), pages 168-183.
    14. Balcombe, Kelvin & Fraser, Iain, 2024. "A Note on an Alternative Approach to Experimental Design of Lottery Prospects," MPRA Paper 119743, University Library of Munich, Germany.
    15. Balcombe, Kelvin & Bardsley, Nicholas & Dadzie, Sam & Fraser, Iain, 2019. "Estimating parametric loss aversion with prospect theory: Recognising and dealing with size dependence," Journal of Economic Behavior & Organization, Elsevier, vol. 162(C), pages 106-119.
    16. Georgalos, Konstantinos, 2021. "Dynamic decision making under ambiguity: An experimental investigation," Games and Economic Behavior, Elsevier, vol. 127(C), pages 28-46.
    17. Kelvin Balcombe & Iain Fraser & Abhijit Sharma, 2021. "An econometric analysis of salience theory," Bulletin of Economic Research, Wiley Blackwell, vol. 73(4), pages 545-554, October.
    18. Glenn W. Harrison & J. Todd Swarthout, 2016. "Cumulative Prospect Theory in the Laboratory: A Reconsideration," Experimental Economics Center Working Paper Series 2016-04, Experimental Economics Center, Andrew Young School of Policy Studies, Georgia State University.
    19. Markus Glatt & Roy Brouwer & Ivana Logar, 2019. "Combining Risk Attitudes in a Lottery Game and Flood Risk Protection Decisions in a Discrete Choice Experiment," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 74(4), pages 1533-1562, December.
    20. Konstantinos Georgalos & Nathan Nabil, 2023. "Testing Models of Complexity Aversion," Working Papers 400814269, Lancaster University Management School, Economics Department.

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    More about this item

    Keywords

    Risk; Prospect theory; Rank dependent utility; Bayesian model averaging; Contextual utility; C11; C52; D81;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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