Choice Models: Logit Model Derivation

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (31)

Page 2 of 4

Covers theoretical foundations and methodologies of discrete choice models for predicting behavior and obtaining demand models.

Mixture models: alternative specific variance

Explores alternative specific variance in mixture models and discusses identification issues and model comparisons using 500 draws.

Sampling: conditional maximum likelihood estimation

Covers Conditional Maximum Likelihood estimation, contribution to likelihood, and MEV model application in choice-based samples.

Panel data: static model

Introduces the static model for panel data analysis and discusses its limitations.

Bayesian Estimation: Overview and Examples

Introduces Bayesian estimation, covering classical versus Bayesian inference, conjugate priors, MCMC methods, and practical examples like temperature estimation and choice modeling.

The red bud-blue bus paradox

Discusses the red bud-blue bus paradox in mode choice modeling and its policy implications.

Revenue Maximization: Introduction to Choice Models

Covers revenue maximization in choice models, pricing strategies, market competition, and a binary logit model example.

Binary Response: Link Functions

Explores binary response interpretation, link functions, logistic regression, and model selection using deviances and information criteria.

Logistic Regression: Modeling Binary Response Variables

Explores logistic regression for binary response variables, covering topics such as odds ratio interpretation and model fitting.

Mixture models: summary

Summarizes mixtures of logit models, covering various mixing methods and modeling techniques for taste heterogeneity.