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-se...

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Bibliographic Details
Published in:Empirical economics Vol. 60; no. 1; pp. 309 - 326
Main Authors: Bai, Jushan, Choi, Sung Hoon, Liao, Yuan
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2021
Springer Nature B.V
Subjects:
Analysis of covariance
Approximation
Econometrics
Economic theory
Economic Theory/Quantitative Economics/Mathematical Methods
Economics
Economics and Finance
Finance
Insurance
Longitudinal studies
Machine learning
Management
Matrices
Methods
Panel data
Statistics for Business
Panel data
Banding
Efficiency
Thresholding
Serial correlation
Cross-sectional correlation
Heteroskedasticity
ISSN:0377-7332, 1435-8921
Online Access:Get full text
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Summary: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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ISSN:0377-7332
1435-8921
DOI:10.1007/s00181-020-01977-2