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

Jushan Bai, Sung Hoon Choi, Yuan Liao

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)309-326
Number of pages18
JournalEmpirical Economics
Volume60
Issue number1
DOIs
StatePublished - Jan 2021

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Mathematics (miscellaneous)
  • Social Sciences (miscellaneous)
  • Economics and Econometrics

Keywords

  • Banding
  • Cross-sectional correlation
  • Efficiency
  • Heteroskedasticity
  • Panel data
  • Serial correlation
  • Thresholding

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