Title: Estimation of functional coefficient panel data models with endogenous selectivity and fixed effects Authors:  Alexandra Soberon - Universidad de Cantabria (Spain) [presenting]
Daniel Henderson - University of Alabama (United States)
Juan Manuel Rodriguez-Poo - Universidad de Cantabria (Spain)
Taining Wang - Capital University of Economics and Business (China)
Abstract: A novel estimation approach is developed for functional coefficient panel data models with sample selection and fixed effects. We propose a two-step pairwise approach that avoids strict identification restrictions and addresses individual heterogeneity and selection bias. The first stage estimates the selection equation parameters, while the second stage estimates the regression of interest using a generalized local weighting scheme that removes the sample selection bias asymptotically using the estimates of the previous stage. We establish the asymptotic properties of the proposed estimators under rather weak assumptions and demonstrate the method's superior computational efficiency with respect to existing approaches and finite-sample performance through Monte Carlo simulations.