Discrete choice models are widely used for the analysis of individual choice behavior and can be applied to choice problems in many fields such as economics, engineering, environmental management, urban planning, and transportation. For example, discrete choice modeling is used in marketing research to guide product positioning, pricing, product concept testing, and many other areas of strategic and tactical interest. Recent applications to predict changes in demand and market share include areas such as choice of travel mode, coffee brands, telephone service, soft drinks and other foods, financial services, internet access, and choice of durables such as smartphones, tablets, automobiles, air conditioners, and houses. This program also covers methods for online applications where predictions of individual choice behavior are used as inputs for the online optimization and personalization of advertising, recommendations and promotions.
The methods covered include discrete choice models (logit, nested logit, generalized extreme value, probit, logit mixtures, hybrid choice models), data collection, specification, estimation, statistical testing, forecasting, and application. The covered topics include analysis of revealed and stated preferences data, sampling, simulation-based estimation, discrete panel data, Bayesian estimation, discrete-continuous models, menu choice, and integration of choice models with latent variables models.
The course includes lab sessions where participants are provided with discrete choice software to learn how to use real databases to estimate and test discrete choice models taught in lectures and gain hands-on experience in using new discrete choice techniques for practical applications. By examining actual case studies of discrete choice methods, students will become familiar with problems of model formulation, estimation, testing, and forecasting.
Participant Takeaways
- Understanding discrete choice models and their applications.
- Learning to apply new discrete choice techniques.
- Understanding problems of data collection, model formulation, estimation, testing, and forecasting, as learned through case studies of discrete choice methods.
- Utilizing commonly available software to estimate and test discrete choice models from real databases.
Who Should Attend
This program is intended for academics and professionals interested in learning new discrete choice techniques and how to predict choice and forecast demand. It is particularly suited for academics engaged in research, especially PhD students in economics, planning, civil engineering, management, behavioral science, health science, and political science. This course will also benefit professionals in market research, transportation consulting, planning, and any government agencies.
Participants will gain hands-on experience in applying discrete choice software in real-world case studies. A working knowledge of basic statistical methods is needed.
Scholarships
Half-tuition scholarships are available for junior faculty, postdocs, and doctoral students. To apply for the scholarship, please email a CV and a letter stating the relevance of the course to your research to discretedoctoralscholarship@mit.edu. The deadline to apply for the scholarship is June 8.
Please contact Katie Rosa at discretedoctoralscholarship@mit.edu with any questions.
Discounts for Faculty
In addition, a limited number of partial-tuition scholarships are available for teaching faculty, rank of instructor or higher, at other educational institutions. Decisions are made on a rolling basis after submitting a course registration form and a Scholarship Request Form. Please note that these scholarships are only for tuition and do not cover travel, lodging, or other expenses associated with the course.
If you have any questions please contact the Short Programs office.
Scholarships are available for graduate students and faculty registering for this course.
Requirements
This course will be taught over the Zoom platform. Participants will need to install Biogeme. Tablets will not be sufficient for the computing activities performed in this course.
Program Outline
Class runs 9:30 am - 5:00 pm every day.
There will be a virtual reception for participants and faculty on Monday evening.
The course consists of a series of lectures and lab sessions that develop discrete choice concepts and techniques and demonstrate their applications. The labs offer hands-on experience in applying the material covered in the lectures using discrete choice software and real-world data sets.
Topics Covered Include the Following:
Theories of choice, random utility models, probabilistic choice models, alternative model formulations, statistical estimation procedures appropriate for alternative data sources, currently available computer software, tests of validity, forecasting procedures, and examples of empirical applications.
The following subjects will be addressed during the course:
- Choice Behavior
- Binary Choice Models
- Specification and Estimation of Choice Models
- Discrete Choice and Machine Learning
- Stated Preference Methods
- Multinomial Choice Models: Probit and Logit
- Specification Testing
- Aggregate Forecasting and Microsimulation
- IIA Tests
- Nested Logit Models
- Extreme Value Models
- Sampling and Estimation
- Mixture Models
- Simulation-Based Estimation
- Dynamic Choice Models and Panel Data
- Combining Revealed and Stated Preferences
- Models with Latent Variables
- Choice from a Menu
- Bayesian Estimation
- Online applications
- Endogeneity and Self-Selection
- Choice Behavior and the Measurement of Well-Being
Additional Instructors
Carmine Gioia, PhD
Research Affiliate & Lecturer, Department of Civil & Environmental Engineering, MIT
The type of content you will learn in this course, whether it's a foundational understanding of the subject, the hottest trends and developments in the field, or suggested practical applications for industry.
How the course is taught, from traditional classroom lectures and riveting discussions to group projects to engaging and interactive simulations and exercises with your peers.
What level of expertise and familiarity the material in this course assumes you have. The greater the amount of introductory material taught in the course, the less you will need to be familiar with when you attend.