Explores the application of Maximum Likelihood Estimation in binary choice models, covering probit and logit models, latent variable representation, and specification tests.
Introduces Discrete Choice Analysis, covering scale, depth, data collection, and statistical inference.
Explores the Red bus/Blue bus paradox, nested logit models, and multivariate extreme value models in transportation.
Explores binary response interpretation, link functions, logistic regression, and model selection using deviances and information criteria.
Explains the derivation of the logit model in choice models, covering error terms, choice sets, and availability conditions.
