1.202 Demand Modeling
Theory and application of modeling and statistical methods for analysis and forecasting of demand for facilities, services, and products. Topics include: review of probability and statistics, estimation and testing of linear regression models, theory of individual choice behavior, derivation, estimation, and testing of discrete choice models (including logit, nested logit, GEV, probit, and mixture models), estimation under various sample designs and data collection methods (including revealed and stated preferences), sampling, aggregate forecasting methods, and iterative proportional fitting and related methods. Lectures reinforced with case studies, which require specification, estimation, testing, and analysis of models using data sets from actual applications.
This class has 1.201 as a prerequisite.
1.202 will be offered this semester (Spring 2018). It is instructed by M. Ben-Akiva.
Lecture occurs 10:30 AM to 12:00 PM on Mondays and Wednesdays in 1-150.
This class counts for a total of 12 credits. This is a graduate-level class.
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