All at once! A comprehensive and tractable semi-parametric method to elicit prospect theory components

Eliciting all the components of prospect theory – curvature utility function, weighting function and loss aversion remains an open empirical challenge. We develop a semi-parametric method that keeps tractability parametric methods while providing more precise estimates. Applying new to datasets Tversky Kahneman (1992) Bruhin et al. (2010), we reject convexity in domain show probability does not exhibit duality equality across domains, line with cumulative contrast original rank dependent theories. Furthermore, our highlights overweighting tail probabilities is pronounced gain than domain. Overall, results varies little thus suggesting distortions are key capture differences risk attitudes domains.