随机系数潜在效用模型的辨识
Identification of Random Coefficient Latent Utility Models
摘要：
本文给出了扰动效用模型中随机系数分布的非参数辨识结果。我们涵盖离散和连续选择模型。我们使用平均数量的变化来建立识别，当分析师观察总需求时，结果适用，但不适用于商品是否被一起选择。我们需要排除限制和独立的随机斜率系数和随机截距。我们不要求回归变量具有大的支持或参数假设。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
