Title: Inference on panel data models with a generalized factor structure Authors:  Alexandra Soberon - Universidad de Cantabria (Spain)
Stefan Sperlich - University of Geneva (Switzerland)
Juan Manuel Rodriguez-Poo - Universidad de Cantabria (Spain) [presenting]
Abstract: The focus is on the identification, inference, and validation of linear panel data models when a nonparametric function accounts for both factors and factor loadings. 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 provide 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.