Comparer des méthodes

	GLS avec Ruptures Structurelles	Moindres Carrés Généralisés Robustes (MCG Robustes)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1998 (structural break GLS formalization)	1936 / 1980
Auteur d'origine	Bai & Perron (1998); GLS framework by Aitken (1936)	Aitken (GLS theory, 1936); White (robust covariance, 1980)
Type	Regression estimator	Robust linear regression
Source fondatrice	Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47–78.	Greene, W. H. (2012). Econometric Analysis (7th ed.). Pearson. Chapter 9: The Generalized Regression Model and Heteroscedasticity. ISBN: 978-0131395381
Alias	GLS with structural breaks, break-adjusted GLS, structural change GLS, regime-switching GLS	robust generalized least squares, GLS with robust standard errors, heteroscedasticity-consistent GLS, HC-GLS
Apparentées	6	5
Résumé	Structural Break GLS combines Generalized Least Squares estimation with explicit allowance for regime shifts in the data-generating process. The method estimates separate coefficient vectors for each segment defined by detected break dates while correcting for non-spherical errors — heteroscedasticity or autocorrelation — that frequently accompany structural change, yielding consistent and efficient estimates across all regimes.	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.