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 (FGLS) estimator is robust to heteroskedasticity, serial correlation, and cross-sectional correlation. It is more efficient than the OLS estimator. To control serial correlation , we employ the banding method. To control cross-sectional correlation, without knowing the clusters, we suggest using the thresholding method. We establish the consistency of the proposed estimator. A Monte Carlo study is considered. The proposed method is applied to an empirical application.
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 (FGLS) estimator is more efficient than the ordinary least squares (OLS) in the presence of heteroskedasticity, serial and cross-sectional correlations. To take into account the serial correlations , we employ the banding method. To take into account the cross-sectional correlations, we suggest to use the thresholding method. We establish the consistency of the proposed estimator. A Monte Carlo study is considered. The proposed method is applied to an empirical application.
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correlation, without knowing the clusters, we suggest using
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