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

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.

Automated summary

This paper presents a feasible generalized least squares (GLS) estimator for linear panel data models, which is more efficient than ordinary least squares in the presence of heteroskedasticity, serial, and cross-sectional correlations. The covariance matrix for the feasible GLS is estimated using the banding and thresholding method. The proposed estimator's limiting distribution is established, and it is validated through a Monte Carlo study and empirical application.