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Robust GLS

Robust GLS extends classical Generalized Least Squares by pairing GLS coefficient estimation with heteroscedasticity- and autocorrelation-consistent (HAC) standard errors, or by using M-estimation within the GLS framework. It corrects for non-spherical errors — heteroscedasticity, autocorrelation, or both — while also guarding inference against misspecification of the error covariance structure.

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Robust Generalized Least Squares

Taksonoomiline meetodikirje · regression-model / econometrics

Greene, W. H. (2012). Econometric Analysis (7th ed.). Pearson. Chapter 9: The Generalized Regression Model and Heteroscedasticity. · ISBN 978-0131395381
White, H. (1980). A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity. Econometrica, 48(4), 817-838. · DOI 10.2307/1912934

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Same method familyGeneralized Least Squaresmachine-suggested · Relational suggestion, not evidence.Same method familyOLS Regressionmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketPanel GLSmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketRobust OLSmachine-suggested · Relational suggestion, not evidence.Same method familyWeighted Least Squaresmachine-suggested · Relational suggestion, not evidence.