Welcome to my GitHub Page
This contains Python code related to separable matching with transferable utilities, as analyzed in my paper with Alfred Galichon.
At this stage, it has code for our Iterative Projection Fitting Procedure (IPFP) to solve for equilibrium in a Choo and Siow 2006 model of bipartite, one-to-one matching with perfectly transferable utility. My ipfp_python project has IPFP solvers for several variants of the Choo and Siow model: with or without singles, homoskedastic and heteroskedastic. It also contains code for a Streamlit interactive app that demonstrates the basic model (homoskedastic, with singles). You can try it here.
I plan to add code that uses moment matching to estimate the semilinear Choo and Siow model as per Proposition 5 of the Galichon-Salanie paper.