Discrete Choice under Risk with Limited Consideration
提出一个离散选择模型，允许考虑集和风险厌恶存在未观测异质性，给出半非参数点识别的充分条件，并提供一个易计算的估计量，适用于大选择集市场，用财产保险数据验证。
This paper is concerned with learning decision-makers’ preferences using data on observed choices from a finite set of risky alternatives. We propose a discrete choice model with unobserved heterogeneity in consideration sets and in standard risk aversion. We obtain sufficient conditions for the model’s semi-nonparametric point identification, including in cases where consideration depends on preferences and on some of the exogenous variables. Our method yields an estimator that is easy to compute and is applicable in markets with large choice sets. We illustrate its properties using a dataset on property insurance purchases.