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

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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 dimen-sionality\'.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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