This paper studies the estimation of the partially linear panel data models, allowing for cross-sectional dependence through a common factors structure. This semiparametric additive partial linear framework, including both linear and nonlinear additive components, is more flexible compared to linear models, and is preferred to a fully nonparametric regression because of the ‘curse of dimensionality’. The consistency and asymptotic normality of the proposed estimators are established for the case where both cross-sectional dimension and temporal dimension go to infinity. The theoretical findings are further supported for small samples via a Monte Carlo study. The results suggest that the proposed method is robust to a wide variety of data generation processes.
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This research was supported by the National Natural Science Foundation of China under Grant Nos. 71703156, 71988101, and 72073126.
This paper was recommended for publication by Editor CAI Zongwu.
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Huang, B., Sun, Y. & Wang, S. Estimation of Partially Linear Panel Data Models with Cross-Sectional Dependence. J Syst Sci Complex (2021). https://doi.org/10.1007/s11424-021-0122-4
- Common correlated effects
- common factors
- cross-sectional dependence
- panel data
- semi-parametric estimation