Flexible Covariance Structures for Categorical Dependent Variables Through Finite Mixtures of Generalized Extreme Value Models
提出一类新的有限混合离散选择模型FinMix，通过组合多个广义极值模型实现更灵活的误差协方差结构，并验证其与效用最大化理论的兼容性，适用于经济评估中的模型选择。
A new class of finite mixture discrete choice models, denoted FinMix (fīn mĭks), is introduced. These arise from the combination of a finite number of core Generalized Extreme Value (GEV) models to achieve more flexible functional forms, particularly in terms of error covariance structures. Example members of the class include combinations of (1) Multinomial Logit (MNL) models with differing scales, (2) multinomial logit with nested MNL models, (3) tree extreme value models with differing preference trees, and so on. Compatibility of FinMix models with utility maximization is easily determined, which permits empirical investigation of the suitability of specific model forms for economic evaluation exercises.