Christophe Gaillac: Partially Linear Models under Data Combination (with Xavier D'Haultfoeuille and Arnaud Maurel)
We consider the identification and inference on a partially linear model, in a data combination environment where researchers have access to two different datasets that can not be matched. This problem arises frequently in various fields in economics, including consumption, education and health. In situations where all of the regressors are available in one of the datasets, which includes two-sample 2SLS as a special case, we use recent results from optimal transport to derive a constructive characterization of the sharp identified set. We build on this result to develop a tractable inference method that is shown to perform well in finite samples. In situations where the regressors are not jointly observed, we propose a tractable characterization of an outer identified set. We show that this set can be quite informative in practice, coincides with the sharp identified set when the distributions are normal, and is amenable to inference using existing tools from the moment inequality literature. Finally, we apply our methodology to study multigenerational income mobility using data from the PSID.
Date: 26 November 2021, 14:15 (Friday, 7th week, Michaelmas 2021)
Venue: Manor Road Building, Manor Road OX1 3UQ
Venue Details: Seminar Room C or Join Zoom Meeting https://zoom.us/j/95783544125?pwd=WnpBTW1DWStZV1lkVlE0dm83Y3JsUT09
Speaker: Christophe Gaillac (University of Oxford)
Organising department: Department of Economics
Part of: Nuffield Econometrics Seminar
Booking required?: Not required
Audience: Members of the University only
Editor: Emma Heritage