Introduction to Discrete Choice Models

This module introduces students to the fundamental concepts of behavior modeling. It likely covers the basics of how human behavior can be predicted and modeled mathematically, setting the foundation for more complex discrete choice models.

This module delves into the theoretical underpinnings of discrete choice models. Students will likely learn about the mathematical and statistical principles that form the basis for these models, including probability theory and utility maximization concepts.

This module focuses on binary choice models, where individuals choose between two alternatives. Students will likely learn how to formulate, analyze, and interpret models for scenarios with two distinct options.

This module introduces students to Biogeme, a software tool for estimating choice models. Students will likely learn how to use this software to implement and analyze discrete choice models using real data sets.

This module expands on binary choice to cover models with multiple alternatives. Students will likely learn about multinomial and nested logit models, and how to handle scenarios with more than two options.

This module covers methods for testing and validating discrete choice models. Students will likely learn about statistical tests, model fit measures, and techniques for assessing the reliability and accuracy of their models.

This final module focuses on using discrete choice models for forecasting future behavior. Students will likely learn how to apply their models to predict choices and demand in various scenarios, and how to interpret and present these forecasts.