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Feasible Generalized Least Squares for Panel Data with Cross-sectional and Serial Correlations

Jushan Bai, Sung Hoon Choi, Yuan Liao

arXiv 20 Oct 2019 · Econometrics · publishedEmpirical Economics (2020) · 292 citations (OpenAlex)

arXiv:1910.09004 · PDF · DOI · OpenAlex · Extracted main text

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 (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.

Citation extraction

35
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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Bai, Choi, and Liao (2019) Standard Errors for Panel Data Models with Unknown Clusters self1.00054100%
2Arellano (1987) Computing Robust Standard Errors for Within-groups Estimators0.84333100%
3White (1980) A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity0.84333100%
4Bickel and Levina (2008) Covariance regularization by thresholding0.81142100%
5Newey and West (1994) Automatic lag selection in covariance matrix estimation0.81142100%
6Wolfers (2006) Did unilateral divorce laws raise divorce rates? A reconciliation and new results0.69371100%
7Hansen (1982) Large sample properties of generalized method of moments estimators0.64422100%
8Miller and Startz (2018) Feasible Generalized Least Squares Using Machine Learning0.64422100%
9Newey (1990) Efficient instrumental variables estimation of nonlinear models0.64422100%
10Newey and McFadden (1994) Large sample estimation and hypothesis testing0.64422100%

Showing the top 10 of 37 scored citations.