Deterministic Part: Utility Function Coding

Explores the Red bus/Blue bus paradox, nested logit models, and multivariate extreme value models in transportation.

Mixture Models: Simulation-based Estimation

Explores mixture models, including discrete and continuous mixtures, and their application in capturing taste heterogeneity in populations.

Derivation of the logit model

Delves into the logit model's derivation, emphasizing the importance of the independence assumption and parameter normalization during estimation.

Maximum Likelihood Estimation: Theory

Covers the theory behind Maximum Likelihood Estimation, discussing properties and applications in binary choice and ordered multiresponse models.

MLE Applications: Binary Choice Models

Explores the application of Maximum Likelihood Estimation in binary choice models, covering probit and logit models, latent variable representation, and specification tests.