Parametric Recoverability of Preferences
将显示偏好理论用于从一致和不一致的消费者选择中恢复近似参数偏好，提出衡量选择隐含偏好排序与参数偏好排序不一致的指标，并验证其优于标准距离方法。
Revealed preference theory is brought to bear on the problem of recovering approximate parametric preferences from consistent and inconsistent consumer choices. We propose measures of the incompatibility between the revealed preference ranking implied by choices and the ranking induced by the considered parametric preferences. These incompatibility measures are proven to characterize well-known inconsistency indices. We advocate a recovery approach that is based on such incompatibility measures and demonstrate its applicability for misspecification measurement and model selection. Using an innovative experimental design, we empirically substantiate that the proposed revealed-preference-based method predicts choices significantly better than a standard distance-based method.