Direct Semiparametric Estimation of Fixed Effects Panel Data Varying Coefficient Models

In this paper we present a new technique to estimate varying coefficient models of unknown form in a panel data framework where individual effects are arbitrarily correlated with the explanatory variables in a unknown way. The resulting estimator is robust to misspecification in the functional form of the varying parameters and it is shown to be consistent and asymptotically normal. Furthermore, introducing a transformation, it achieves the optimal rate of convergence for this type of problems and it exhibits the so called "oracle" efficiency property. Since the estimation procedure depends on the choice of a bandwidth matrix, we also provide a method to compute this matrix empirically. Monte Carlo results indicates good performance of the estimator in finite samples