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

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  • Jushan Bai

    (Columbia University)

  • Sung Hoon Choi

    (Rutgers University)

  • Yuan Liao

    (Rutgers University)

Abstract

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.

Suggested Citation

  • Jushan Bai & Sung Hoon Choi & Yuan Liao, 2021. "Feasible generalized least squares for panel data with cross-sectional and serial correlations," Empirical Economics, Springer, vol. 60(1), pages 309-326, January.
    • Handle: RePEc:spr:empeco:v:60:y:2021:i:1:d:10.1007_s00181-020-01977-2
    DOI: 10.1007/s00181-020-01977-2
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