论文标题
识别随机系数潜在实用程序模型
Identification of Random Coefficient Latent Utility Models
论文作者
论文摘要
本文为扰动的实用程序模型中的随机系数分布提供了非参数识别结果。我们涵盖离散和连续的选择模型。我们使用平均数量变化建立识别，并且当分析人员观察总需求而不是选择货物时，结果适用。我们需要排除限制和随机斜率系数和随机截距之间的独立性。我们不需要回归器具有较大的支持或参数假设。
This paper provides nonparametric identification results for random coefficient distributions in perturbed utility models. We cover discrete and continuous choice models. We establish identification using variation in mean quantities, and the results apply when an analyst observes aggregate demands but not whether goods are chosen together. We require exclusion restrictions and independence between random slope coefficients and random intercepts. We do not require regressors to have large supports or parametric assumptions.
