Estimating Separable Matching Models

Explore a 27-minute lecture on estimating separable matching models presented by Bernard Salanié from Columbia University. Delve into two simple methods for estimating models in the class of matching with transferable utility that impose a natural restriction on joint surplus separability. Examine the first method, a minimum distance estimator based on the generalized entropy of matching, and the second method, which reformulates the Choo and Siow model as a generalized linear model with two-way fixed effects. Gain insights into these easy-to-apply and highly effective estimation techniques, which are crucial for empirical applications in matching theory and market design.