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Estimation of Fixed Effects Partially Linear Varying Coefficient Panel Data Regression Model with Nonseparable Space-Time Filters

Author

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  • Bogui Li

    (School of Mathematics and Statistics, Fujian Normal University, Fuzhou 350117, China)

  • Jianbao Chen

    (School of Mathematics and Statistics, Fujian Normal University, Fuzhou 350117, China)

  • Shuangshuang Li

    (School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang 471000, China)

Abstract

Space-time panel data widely exist in many research fields such as economics, management, geography and environmental science. It is of interest to study the relationship between response variable and regressors which come from above fields by establishing regression models. This paper introduces a new fixed effects partially linear varying coefficient panel data regression model with nonseparable space-time filters. On the basis of approximating the varying coefficient functions with a powerful B-spline method, the profile quasi-maximum likelihood estimators of parameters and varying coefficient functions are constructed. Under some regular conditions, we derive their consistency and asymptotic normality. Monte Carlo simulation shows that our estimates have good finite performance and ignoring spatial and serial correlations may lead to inefficiency of estimates. Finally, the driving forces of Chinese resident consumption rate are studied using our estimation method.

Suggested Citation

  • Bogui Li & Jianbao Chen & Shuangshuang Li, 2023. "Estimation of Fixed Effects Partially Linear Varying Coefficient Panel Data Regression Model with Nonseparable Space-Time Filters," Mathematics, MDPI, vol. 11(6), pages 1-24, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:6:p:1531-:d:1103643
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    References listed on IDEAS

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    Cited by:

    1. Li, Bogui & Chen, Hao, 2024. "Estimation of fixed effects partially linear varying coefficient spatial autoregressive model with disturbances correlated in space and time," Finance Research Letters, Elsevier, vol. 59(C).

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