Title: Efficient estimation in varying coefficient panel data model with different smoothing variables and fixed effects
Authors: Feng Yao - West Virginia University (United States) [presenting]
Abstract: A varying coefficient panel data model is proposed to be estimated with different smoothing variables and fixed effects using a two step local linear regression approach. The pilot estimator removes fixed effect using kernel-based weight and estimates the varying coefficients by a marginal integration approach. We then use the pilot estimator to perform a one-step backfitting, which is shown to be efficient in the sense of being equivalent to a procedure knowing the other components of the varying coefficient. We obtain the asymptotic properties of both the pilot and efficient estimators. The Monte Carlo simulations show that our proposed estimator performs well. We illustrate their applicability by estimating a varying coefficient panel data production frontier, without assuming functional forms for distribution of efficiency and error terms.