Inference on panel data models with a generalized factor structure

Abstract: We consider identification, inference and validation of linear panel data models when both factors and factor loadings are accounted for by a nonparametric function. This general specification encompasses rather popular models such as the two-way fixed effects and the interactive fixed effects ones. By applying a conditional mean independence assumption between unobserved heterogeneity and the covariates, we obtain consistent estimators of the parameters of interest at the optimal rate of convergence, for fixed and large $T$. We also provide a specification test for the modeling assumption based on the methodology of conditional moment tests and nonparametric estimation techniques. Using degenerate and nondegenerate theories of U-statistics we show its convergence and asymptotic distribution under the null, and that it diverges under the alternative at a rate arbitrarily close to $\sqrt{NT}$. Finite sample inference is based on bootstrap. Simulations reveal an excellent performance of our methods and an empirical application is conducted.