2P Discrete Choice Models in the Social Sciences
Garret Glasgow, University of California, Santa Barbara
22 July - 2 August (two week course / 35 hrs)
Detailed Course Outline [PDF]
Course Content
This course introduces the main techniques for analyzing models with discrete dependent variables. It begins with a discussion of the theoretical foundations of discrete choice models, and then moves on to introduce logit and probit models. We then cover proper interpretation and hypothesis testing with discrete choice models and the basics of maximum likelihood estimation. The course then moves on to consider more complicated discrete choice models, including ordered logits and probits, multinomial logits, and conditional logits. The course concludes with an overview of advanced techniques such as models for repeated choices and mixed logits.
Course Objectives
At the conclusion of this course participants will be able to understand the discrete choice models most commonly used in the social sciences, and properly apply and interpret these models in their own work.
Course Prerequisites
Participants are assumed to have a solid background in probability and statistical inference, and a working knowledge of multiple linear regression, including hands-on experience estimating regression models with statistical software. Some familiarity with linear algebra and statistical software is assumed, but is not essential to success in the course.
Representative Background Reading
Gujarati, Damodar N. (2009) Essentials of Econometrics, 4th Edition. Irwin/McGraw-Hill.