no code implementations • 13 Feb 2024 • Jackson Bunting, Paul Diegert, Arnaud Maurel
We provide semiparametric identification results for a broad class of learning models in which continuous outcomes depend on three types of unobservables: i) known heterogeneity, ii) initially unknown heterogeneity that may be revealed over time, and iii) transitory uncertainty.
1 code implementation • 11 Apr 2022 • Xavier D'Haultfœuille, Christophe Gaillac, Arnaud Maurel
We study partially linear models when the outcome of interest and some of the covariates are observed in two different datasets that cannot be linked.
no code implementations • 3 Oct 2019 • Shakeeb Khan, Arnaud Maurel, Yichong Zhang
Our main findings are that imposing a factor structure yields point identification of parameters of interest, such as the coefficient associated with the endogenous regressor in the outcome equation, under weaker assumptions than usually required in these models.
