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

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.