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Estimation of panel data partially linear time-varying coefficient models with cross-sectional spatial autoregressive errors

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  • Yan-Yong Zhao

    (Southeast University
    Nanjing Audit University)

  • Ling-Ling Ge

    (Nanjing Audit University)

  • Yuan Liu

    (Southeast University
    Nanjing Audit University)

Abstract

A more efficient estimation procedure on a panel data partially linear time-varying coefficient model (PDPLTVCM) with both fixed effects and spatial autoregressive errors is discussed in this paper. Without taking the first-order difference, we develop a new procedure for estimating the autoregressive parameter by taking a dummy variate-based semiparametric least-squares estimation (SLSE) approach and a new generalized method of moments (GMM) method. Asymptotic properties of the resultant estimators are established under some mild assumptions. Further, we derive the weighted semiparametric estimators for both the parameters and coefficient functions, and the main results show that they have the optimal convergence rate and are more efficient than the unweighted versions. Some Monte Carlo experiments are conducted to evaluate the finite sample performance of the proposed methods, and an authentic data example is investigated for illustration.

Suggested Citation

  • Yan-Yong Zhao & Ling-Ling Ge & Yuan Liu, 2025. "Estimation of panel data partially linear time-varying coefficient models with cross-sectional spatial autoregressive errors," Statistical Papers, Springer, vol. 66(1), pages 1-37, January.
    • Handle: RePEc:spr:stpapr:v:66:y:2025:i:1:d:10.1007_s00362-024-01620-7
    DOI: 10.1007/s00362-024-01620-7
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    References listed on IDEAS

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    1. Kelejian, Harry H. & Prucha, Ingmar R., 2010. "Specification and estimation of spatial autoregressive models with autoregressive and heteroskedastic disturbances," Journal of Econometrics, Elsevier, vol. 157(1), pages 53-67, July.
    2. Su, Liangjun, 2012. "Semiparametric GMM estimation of spatial autoregressive models," Journal of Econometrics, Elsevier, vol. 167(2), pages 543-560.
    3. Feng, Guohua & Gao, Jiti & Peng, Bin & Zhang, Xiaohui, 2017. "A varying-coefficient panel data model with fixed effects: Theory and an application to US commercial banks," Journal of Econometrics, Elsevier, vol. 196(1), pages 68-82.
    4. Peng, Bin & Shen, Xinyuan & Ye, Jinqi, 2019. "Testing for sphericity in a fixed effects panel data model with time-varying variances," Economics Letters, Elsevier, vol. 181(C), pages 85-89.
    5. Xuan Liang & Jiti Gao & Xiaodong Gong, 2022. "Semiparametric Spatial Autoregressive Panel Data Model with Fixed Effects and Time-Varying Coefficients," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(4), pages 1784-1802, October.
    6. Cai, Zongwu & Xiao, Zhijie, 2012. "Semiparametric quantile regression estimation in dynamic models with partially varying coefficients," Journal of Econometrics, Elsevier, vol. 167(2), pages 413-425.
    7. Chen, Jia & Gao, Jiti & Li, Degui, 2012. "Semiparametric trending panel data models with cross-sectional dependence," Journal of Econometrics, Elsevier, vol. 171(1), pages 71-85.
    8. Yan, Zheming & Zou, Baoling & Du, Kerui & Li, Ke, 2020. "Do renewable energy technology innovations promote China's green productivity growth? Fresh evidence from partially linear functional-coefficient models," Energy Economics, Elsevier, vol. 90(C).
    9. Kottaridi, Constantina & Stengos, Thanasis, 2010. "Foreign direct investment, human capital and non-linearities in economic growth," Journal of Macroeconomics, Elsevier, vol. 32(3), pages 858-871, September.
    10. Jia Chen & Jiti Gao & Degui Li, 2013. "Estimation in Partially Linear Single-Index Panel Data Models With Fixed Effects," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(3), pages 315-330, July.
    11. Xue, Liugen & Zhu, Lixing, 2007. "Empirical Likelihood for a Varying Coefficient Model With Longitudinal Data," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 642-654, June.
    12. Apeti, Ablam Estel & Edoh, Eyah Denise, 2023. "Tax revenue and mobile money in developing countries," Journal of Development Economics, Elsevier, vol. 161(C).
    13. Kapoor, Mudit & Kelejian, Harry H. & Prucha, Ingmar R., 2007. "Panel data models with spatially correlated error components," Journal of Econometrics, Elsevier, vol. 140(1), pages 97-130, September.
    14. Hu, Xuemei, 2017. "Semi-parametric inference for semi-varying coefficient panel data model with individual effects," Journal of Multivariate Analysis, Elsevier, vol. 154(C), pages 262-281.
    15. Ablam Estel Apeti & Eyah Denise Edoh, 2023. "Tax revenue and mobile money in developing countries," Post-Print hal-04712070, HAL.
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