19-786 Stochastic Discrete Choice Models: Estimation and Behavioral Theory

12 units, Carnegie Mellon University

This course will cover the rational and behavioral foundations of discrete choice models, current behavioral theories, and estimation methods. Content will include an overview of the history of thinking about discrete choice models, rational foundations, behavioral theories, signal detection theory, multinomial logit, mixed logit using restricted MLE and montecarlo simulation, and experimental design. If time permits we will cover itemresponse models and Bayesian methods.

Prerequisites: none listed

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