Testing for the Significance of Violations of Afriat's Inequalities
提出两种新非参数方法，用于评估标准非随机检验发现的显示偏好违背的显著性。这些方法能高概率正确检测存在测量误差数据中的效用最大化，且对误差设定不敏感，对随机行为有检验力。
Two new nonparametric procedures are developed to evaluate the significance of violations of revealed preference found by standard nonstochastic tests. Our tests with high probability correctly detect utility maximization for data generated with measurement error. The procedures are not very sensitive to misspecifying the amount of error that could have caused the data to violate revealed preference. The tests have power against an alternative of random behavior. Both tests fail to reject the null of rational utility maximization from a monetary dataset that has violations of revealed preference.