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Feasible generalized least squares for panel data with cross-sectional and serial correlations

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This paper considers generalized least squares (GLS) estimation for linear panel data models. By estimating the large error covariance matrix consistently, the proposed feasible GLS estimator is more efficient than the ordinary least squares in the presence of heteroskedasticity, serial and cross-sectional correlations. The covariance matrix used for the feasible GLS is estimated via the banding and thresholding method. We establish the limiting distribution of the proposed estimator. A Monte Carlo study is considered. The proposed method is applied to an empirical application.

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  1. For technical simplicity, we focus on a simple model where there are no fixed effects. It is straightforward to allow additive fixed effects \(\alpha _i+\mu _t\) by applying the de-meaning first. The theories would be slightly more sophisticated, though such extensions are straightforward.


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Correspondence to Yuan Liao.

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Bai, J., Choi, S.H. & Liao, Y. Feasible generalized least squares for panel data with cross-sectional and serial correlations. Empir Econ 60, 309–326 (2021).

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