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

Author

Listed:
  • Hua Liu

    (Shanghai University of Finance and Economics)

  • Youquan Pei

    (Shandong University)

  • Qunfang Xu

    (Ningbo University)

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.

Suggested Citation

  • Hua Liu & Youquan Pei & Qunfang Xu, 2020. "Estimation for varying coefficient panel data model with cross-sectional dependence," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 83(3), pages 377-410, April.
    • Handle: RePEc:spr:metrik:v:83:y:2020:i:3:d:10.1007_s00184-019-00739-0
    DOI: 10.1007/s00184-019-00739-0
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    References listed on IDEAS

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