Chambers College of Business and Economics
We propose a varying coefficient regression model for panel data that controls for both latent heterogeneities in cross-sectional units and unobserved common shocks over time. The model allows different smoothing variables to enter through either a stand-alone function or a coefficient function. Without requiring a normalization of the fixed effects, we propose a two-step estimator. First, we estimate the varying coefficients with the pilot series-based estimators, eliminating fixed effects though differencing. Second, we perform a one-step kernel backfitting to improve the estimation efficiency. We demonstrate through Monte-Carlo simulations that our estimators are computationally efficient and perform well relative to a profile-based kernel estimator.
Digital Commons Citation
Wang, Taining and Yao, Feng, "A varying coefficient model with two-way fixed effects and different smoothing variables" (2021). Economics Faculty Working Papers Series. 54.