Efficient Estimation in Varying Coefficient Panel Data Model with Different Smoothing Variables and Fixed Effects

Abstract

The authors propose to estimate a varying coefficient panel data model with different smoothing variables and fixed effects using a two-step approach. The pilot step estimates the varying coefficients by a series method. We then use the pilot estimates to perform a one-step backfitting through local linear kernel smoothing, which is shown to be oracle efficient in the sense of being asymptotically equivalent to the estimate knowing the other components of the varying coefficients. In both steps, the authors remove the fixed effects through properly constructed weights. The authors obtain the asymptotic properties of both the pilot and efficient estimators. The Monte Carlo simulations show that the proposed estimator performs well. The authors illustrate their applicability by estimating a varying coefficient production frontier using a panel data, without assuming distributions of the efficiency and error terms.

Citation

Yao, F., Lu, Q., Sun, Y. and Zhang, J. (2024), "Efficient Estimation in Varying Coefficient Panel Data Model with Different Smoothing Variables and Fixed Effects", Parmeter, C.F., Tsionas, M.G. and Wang, H.-J. (Ed.) Essays in Honor of Subal Kumbhakar (Advances in Econometrics, Vol. 46), Emerald Publishing Limited, Leeds, pp. 133-184. https://doi.org/10.1108/S0731-905320240000046007