  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
limiting distribution of the proposed estimator. A Monte Carlo study is
considered. The proposed method is applied to an empirical application.
