All at Once! A Comprehensive and Tractable Semi-Parametric Method to Elicit Prospect Theory Components
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20 novembre 2020
- handle: 10670/1.d8dt6r
- halshs: halshs-03016517
info:eu-repo/semantics/OpenAccess
Mots-clés
Prospect theory semi-parametric estimation risk attitudes weighting function loss aversion D - Microeconomics/D.D8 - Information, Knowledge, and Uncertainty/D.D8.D81 - Criteria for Decision-Making under Risk and Uncertainty C - Mathematical and Quantitative Methods/C.C9 - Design of Experiments/C.C9.C91 - Laboratory, Individual BehaviorSujets proches
Probability Statistical inference Aversion, LossCiter ce document
Yao Thibaut Kpegli et al., « All at Once! A Comprehensive and Tractable Semi-Parametric Method to Elicit Prospect Theory Components », HAL-SHS : économie et finance, ID : 10670/1.d8dt6r
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Résumé
Eliciting all the components of prospect theory-curvature of the utility function, weighting function and loss aversion-remains an open empirical challenge. We develop a semi-parametric method that keeps the tractability of parametric methods while providing more precise estimates. Using the data of Tversky and Kahneman (1992), we revisit their main parametric results. We reject the convexity of the utility function in the loss domain, find lower probability weighting, and confirm loss aversion. We also report that the probability weighting function does not exhibit duality and equality across domains, in line with cumulative prospect theory and in contrast with original prospect and rank dependent utility theories.