Individual Choice Behavior: Theory and Application of Discrete Choice Analysis

Venue: MIT Campus - Cambridge, MA

Location: Cambridge, Massachusetts, United States

Event Date/Time: Jun 18, 2007 End Date/Time: Jun 22, 2007
Report as Spam


Discrete choice models are widely used for the analysis of individual choice behavior. Recent applications to predict changes in demand and market shares include areas such as: choice of travel mode, coffee brand, telephone service, soft drinks and other foods, and choice of durables such as automobiles, air conditioners and houses. Discrete (or qualitative) choice analysis was initially developed by researchers in psychology, but has been extended to apply to choice problems in many fields. It is used in marketing research to guide product positioning, pricing, product concept testing, and many other areas of strategic and tactical interest.

This one-week program undertakes an in-depth study of discrete choice models and their applications. It provides participants with the practical tools necessary for applying new discrete choice techniques. By examining actual case studies of discrete choice methods, students will be familiarized with problems of data collection, model formulation, testing, and forecasting, and will gain hands-on application experience by applying freely available software to estimate and test discrete choice models from real databases.

Who Should Attend
This program is intended for marketing researchers and analysts, economists, operations researchers, engineers, planners, managers and industry, government and academic researchers who are interested in understanding and predicting consumer choices, demand and market share. Participants should have a working knowledge of basic statistical methods.

Topics Covered Include the Following
The material covered includes: 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.

More specifically, the following subjects will be addressed during the course:

Choice Behavior
Binary Choice Models
Stated Preference Surveys
Multinomial Choice Models: Properties of Probit, Logit and Discriminant Analysis
Specification and Estimation of Discrete Choice Models
Statistical Tests of Discrete Choice Models
Forecasting and Micro-Simulation
Nested Logit Models
Multi-variate(Generalized) Extreme Value Models
Mixture Models, such as Logit Kernel or Mixed Logit
Simulation-based Estimation
Bayesian Estimation
Discrete Panel Data
Combining Revealed and Stated Preferences
Sampling Strategies for Discrete Choice Analysis
Joint Discrete / Continuous Models
Choice from a Menu
Choice Models with Latent Variables