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This course introduces participants to discrete choice models. These econometric models are used to explain how people choose between discrete outcomes, such as mode of travel to work or type of treatment for pain. The course will cover the subset of discrete choice models known as random utility models, namely the multinomial logit and nested logit. These models are often used in disciplines such as economics, transportation, and public health. No prior knowledge of discrete choice modeling is expected. Hands-on exercises will be conducted in Python.

This 2-part (7/15/24 & 7/17/24) 5-hour course will be offered via Zoom, over two mornings. Attendance is required as the course will not be recorded.

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  • Christian Osei
  • Ivan Suvorov

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