Heterogeneous Choice Sets and Preferences
提出一种在代理人的选择集不可观测时进行离散选择分析的稳健方法，仅假设选择集的最小规模，并允许选择集与偏好之间存在任意依赖关系。利用汽车碰撞保险免赔额数据，发现低风险厌恶和异质性非单元素选择集可解释数据，且超过四分之三的家庭需要有限选择集。
We propose a robust method of discrete choice analysis when agents' choice sets are unobserved. Our core model assumes nothing about agents' choice sets apart from their minimum size. Importantly, it leaves unrestricted the dependence, conditional on observables, between choice sets and preferences. We first characterize the sharp identification region of the model's parameters by a finite set of conditional moment inequalities. We then apply our theoretical findings to learn about households' risk preferences and choice sets from data on their deductible choices in auto collision insurance. We find that the data can be explained by expected utility theory with low levels of risk aversion and heterogeneous non‐singleton choice sets, and that more than three in four households require limited choice sets to explain their deductible choices. We also provide simulation evidence on the computational tractability of our method in applications with larger feasible sets or higher‐dimensional unobserved heterogeneity.