Estimation for varying coefficient panel data model with cross-sectional dependence

Hua Liu, Youquan Pei and Qunfang Xu ()
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Hua Liu: Shanghai University of Finance and Economics
Youquan Pei: Shandong University
Qunfang Xu: Ningbo University

Metrika: International Journal for Theoretical and Applied Statistics, 2020, vol. 83, issue 3, No 6, 377-410

Abstract: Abstract This paper describes a method for estimation and inference with a nonparametric varying coefficients panel data model that allows for cross-sectional dependence and heteroscedasticity, wherein the time series length T is larger than the cross-sectional size N. We first eliminate fixed effects by taking the cross-sectional average, and then use a local linear approach to obtain the initial estimator of the unknown coefficient functions. However, the initial estimator ignores the cross-sectional dependence and heteroscedasticity, which will lead to a loss of efficiency. Thus, we propose a weighted local linear method to obtain a more efficient estimator. In the theoretical part of the paper, we derive the asymptotic theory of the resulting estimator. Simulation results and a real data analysis are provided to illustrate the finite sample performance of the proposed method.

Keywords: Cross-sectional dependence; Local linear method; Panel data model; Three-step generalized kernel approach; Varying coefficient; 62G05; 62G20 (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s00184-019-00739-0

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