Revealed Preferences over Risk and Uncertainty
提出一种非参数方法（GRID），用于检验预算选择数据与风险/不确定性下决策模型的一致性，并利用投资组合实验数据评估多种风险模型的性能。
We develop a nonparametric method, called Generalized Restriction of Infinite Domains (GRID), for testing the consistency of budgetary choice data with models of choice under risk and under uncertainty. Our test can allow for risk-loving and elation-seeking attitudes, or it can require risk aversion. It can also be used to calculate, via Afriat’s efficiency index, the magnitude of violations from a particular model. We evaluate the performance of various models under risk (expected utility, disappointment aversion, rank-dependent utility, and stochastically monotone utility) using data collected from several recent portfolio choice experiments.