Estimating Risk Attitudes and Assessing the Predictive Power of Models of Decision Making Under Uncertainty

Maria Lucchi & Livio Piero Spori

Student thesis: Master thesis

Abstract

We elicit risk preferences based on an experiment with binary choice lotteries performed by Andersen, Harrison, Lau and Rutström in 2009, with a relevant sample of 501 adult Danes. Maximum likelihood methods are used for pooled and individual estimation of three models: expected utility theory, rank dependent expected utility and Yaari’s dual theory. We estimate each model for three different stochastic error terms, to account for noise. This allows us to understand the fitting power of the models considered and we find that rank dependent expected utility has the best goodness of fit among the models in all estimation types. To check for the predictive power, we perform a forecasting assessment of the models and again we find that rank dependent expected utility performed the best among all models considered, for all error term specifications.

EducationsMSc in Advanced Economics and Finance, (Graduate Programme) Final Thesis
LanguageEnglish
Publication date2018
Number of pages100
SupervisorsMorten Lau