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

Feng Yao, Qinling Lu, Yiguo Sun and Junsen Zhang

A chapter in Essays in Honor of Subal Kumbhakar, 2024, vol. 46, pp 133-184 from Emerald Group Publishing Limited

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.

Keywords: Varying coefficient model; panel; fixed effect; production frontier; efficiency; kernel-based backfitting; C14; C23 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eme:aecozz:s0731-905320240000046007

DOI: 10.1108/S0731-905320240000046007

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