Discrete Choice Analysis: Predicting Demand and Market Shares
Date: TBD 2016 | Tuition: TBD | Continuing Education Units (CEUs): TBD
*This course has limited enrollment. Apply early to guarantee your spot.
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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 brand, 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 undertakes an in-depth study of 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.
Fundamentals: Core concepts, understandings, and tools (35%)
Latest Developments: Recent advances and future trends (40%)
Industry Applications: Linking theory and real-world (25%)
Lecture: Delivery of material in a lecture format (80%)
Labs: Demonstrations, experiments, simulations (20%)
Introductory: Appropriate for a general audience (20%)
Specialized: Assumes experience in practice area or field (50%)
Advanced: In-depth explorations at the graduate level (30%)
- Understand discrete choice models and their applications.
- Learn to apply new discrete choice techniques.
- Understand problems of data collection, model formulation, estimation, testing, and forecasting, as learned through case studies of discrete choice methods.
- Utilize commonly available software to estimate and test discrete choice models from real databases.
- Evaluate 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, and forecasting procedures.
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. 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.
The course consists mainly of a series of lectures, supplemented by labs 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
- 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
- Endogeneity and Self-Selection
- Choice Behavior and the Measurement of Well-Being
On the first day of class, participants will receive a copy of Discrete Choice Analysis by Moshe Ben-Akiva and Steven R. Lerman (MIT Press) and a set of additional reading materials covering recent developments and new applications.
Course schedule, registration times, and Special Events
Registration is 8:30 - 9:00 am on Monday.
Class runs 9:30 am - 5:00 pm every day.
Special events include a reception for course participants and faculty on Monday night and a dinner on Thursday evening. All evening activities are included in tuition.
Please note that laptops are required for this course. Participants will need to install Biogeme. Tablets will not be sufficient for the computing activities performed in this course.
Assistant Professor of Marketing, Simon Fraser University, British Columbia
"Discrete choice analysis is one of the most valuable new tools available to marketers interested in understanding and predicting consumers' choices ... It goes beyond current textbook treatments of discrete choice analysis with discussions of state-of-the-art developments in the area and experimental applications."
Senior Research Engineer, NTT Telecommunications Networks Laboratories
"Discrete choice analysis is an effective way to evaluate a new service and forecast future demand. I have applied it to the estimation of user preference ... in order to develop new telecommunication services."
Member of Technical Staff, Bellcore
"This course is an excellent review and introduction to discrete analysis theory, ... provides opportunities for hands-on applications. Further, the instructors are major contributors to this area. I highly recommend it."
coordinator of economic research, peruvian telecommunications regulatory agency
"It's a must-have course for any economist seeking to learn or refresh their knowledge of discrete choice models."
traffic and revenue analyst, cintra us
"Top-notch, concise instruction in lecture. Coverage of topics did not get too detailed and allowed more sub-areas to be uncovered. Course materials were excellent and it was good to come back with many days of notes and a textbook to serve as a resource."
graduate student, texas a&m university
"Ben-Akiva covered a wide range of topics in discrete choice analysis from basic to recent development within a week. Even though the one week is very short to cover all the subjects, his lecture was very intuitively clear and deep. It was a very good experience, and I hope to take another class at MIT if possible."
research assistant, sufg
"The course is one of a kind!"
About the Presenter
Moshe Ben-Akiva is the Edmund K. Turner Professor of Civil and Environmental Engineering at the Massachusetts Institute of Technology (MIT), and Director of the MIT Intelligent Transportation Systems (ITS) Lab. He holds a PhD degree in Transportation Systems from MIT and honorary degrees from the University of the Aegean, the Université Lumiére Lyon, the Royal Institute of Technology (KTH), and the University of Antwerp. His awards include the Lifetime Achievement Award of the International Association for Travel Behavior Research; the Jules Dupuit prize from the World Conference on Transport Research Society (WCTRS); and the Institute of Electrical and Electronics Engineers (IEEE) ITS Society Outstanding Application Award for DynaMIT, a mesoscopic simulator with algorithms for dynamic traffic assignment, traffic predictions, and travel information and guidance. Ben-Akiva has coauthored two books, including the textbook Discrete Choice Analysis, published by MIT Press, and over 200 papers in refereed journals or conference proceedings. He has been a member of over three dozen various scientific committees, advisory boards, and editorial boards. He has worked as a consultant in industries such as transportation, energy, telecommunications, financial services, and marketing for a number of private and public organizations, including Hague Consulting Group, RAND Europe, ChoiceStream, and Cambridge Systematics, where he is a Senior Principal and a member of the Board of Directors.
One full-tuition scholarship will be a warded to an outstanding doctoral student. Half-tuition scholarships are also available for doctoral students. To apply for the scholarship, please email a CV and a letter stating the relevance of the course to your research to email@example.com. The deadline to apply for the scholarship is May 1, 2015. Doctoral student scholarship applicants should not register for the course until the scholarship decisions have been released in early May.
Please contact Katie Rosa at firstname.lastname@example.org 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.
This course takes place on the MIT campus in Cambridge, Massachusetts. We can also offer this course for groups of employees at your location. Please complete the Custom Programs request form for further details.
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