Estimation of Heterogeneous Panel Data Models With an Application to Program Evaluation

This article studies panel data models with two-dimensional unobserved slope heterogeneity and interactive fixed effects. We propose a two-step approach to estimate the parameters in the model. We apply the proposed model to estimate heterogeneous treatment effects at both individual and aggregate levels. Monte Carlo simulations show that the proposed estimators perform remarkably well in finite samples in comparison with some existing methods such as the synthetic control ones. We apply our method to study the effect of economic liberalization on economic growth and find a positive and significant aggregate average treatment effect on the treated.