Identification and Estimation of Discrete Choice Models with Unobserved Choice Sets
提出非参数识别和估计离散选择模型的方法，从重复选择横截面数据中恢复选择集和偏好的联合分布，并应用于即食麦片行业发现忽略未观测选择集会导致偏好估计偏差。
We propose a framework for nonparametric identification and estimation of discrete choice models with unobserved choice sets. We recover the joint distribution of choice sets and preferences from a cross-section of repeated choices. We assume that either the latent choice sets are sparse or that the number of repeated choices is sufficiently large. Sparsity requires the number of possible choice sets to be relatively small. It is satisfied, for instance, when the choice sets are nested or when they form a partition. Our estimation procedure is computationally fast and uses mixed-integer programming to recover the sparse support of choice sets. Analyzing the ready-to-eat cereal industry using a household scanner dataset, we find that ignoring the unobservability of choice sets can lead to incorrect estimates of preferences.