on optimal transport, part II
This intensive course, part of the ‘math+econ+code’ series, is focused on models of demand, matching models, and optimal transport methods, with various applications pertaining to labor markets, economics of marriage, industrial organization, matching platforms, networks, and international trade, from the crossed perspectives of theory, empirics and computation. It will introduce tools from economic theory, mathematics, econometrics and computing, on a needs basis, without any particular prerequisite other than the equivalent of a first year graduate sequence in econ or in applied math.
This second part focuses on the estimation of the models, and the "inverse optimal transport problem".
Because it aims at providing a bridge between theory and practice, the teaching format is somewhat unusual: each teaching “block” will be made of a mix of theory and coding (in Python), based on an empirical application related to the theory just seen. Students will have the opportunity to write their own code, which is expected to be operational at the end of each block. This course is therefore closer to cooking lessons than to traditional lectures
The course will be taught over two consecutive days, June 6-7 2023, 230pm-6pm Paris time / 830am-12pm New York time.
The instructor is Alfred Galichon (professor of economics and of mathematics at NYU and principal investigator of the ERC-funded project 'equiprice' at Sciences Po).
Applications are now open: you can apply here
Lecture: separable models of matching
○ The logit case
Coding session 2: matching estimation
○ Formulation as a generalized linear model
○ Estimation using scikit-learn
Coding session 1: discrete choice methods
○ Generalized linear models
○ Logistic regression as a generalized linear model
○ Computation using scikit-learn
Coding session 3: gravity equation