USP 657 Advanced Data Analysis: Discrete Choice Modeling

Presents the theory and practice underlying the formulation and estimation of models of individual discrete choice behavior with applications to travel, travel related and other choices. Provides students with an understanding of the theory, methods, application and interpretation of multinomial logit (MNL), nested logit and other members of the Generalized Extreme Value (GEV) family of models, as well as an introduction to mixed logit models.