Estimation of Partially Linear Panel Data Models with Cross-Sectional Dependence

Abstract

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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Corresponding author

Correspondence to Yuying Sun.

Additional information

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

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Keywords

  • Common correlated effects
  • common factors
  • cross-sectional dependence
  • panel data
  • semi-parametric estimation