Course #
41430
Section Number
1
Day(s)
Tu
-
Th
Time(s)
3:30pm-4:50pm
Term
Fall 2024
Course Instructor
Syllabus

This course explores how modern developments in machine learning, AI and statistical learning may be leveraged in order to improve regression analysis and reduced-form econometrics more generally. For instance, we provide a modern, optimization-conscious treatment of linear regression, quantile regression, instrumental variables, and Fréchet-Hoeffding bounds. Throughout, we make an effort to produce methods that remain valid even when models are --as they always are-- misspecified. To produce such regression methods, we fetch tools and results from fields such as linear programming, optimal transport, numerical linear algebra, deep learning and reinforcement learning.

Quarter Title Instructor Day(s) Time(s) Syllabus
Fall 2024 Modern Methods for Applied Regression Guillaume Pouliot Tuesday, Thursday 3:30pm-4:50pm Syllabus
Fall 2024 Modern Methods for Applied Regression Guillaume Pouliot Tuesday, Thursday 5:00pm-6:20pm Syllabus