Comparer des méthodes

	GLS avec Ruptures Structurelles	Moindres Carrés Généralisés (MCG)
Domaine	Économétrie	Statistique
Famille	Regression model	Regression model
Année d'origine	1998 (structural break GLS formalization)	1935
Auteur d'origine	Bai & Perron (1998); GLS framework by Aitken (1936)	Alexander Craig Aitken
Type	Regression estimator	Linear estimator
Source fondatrice	Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47–78.	Aitken, A. C. (1935). IV.—On least squares and linear combination of observations. Proceedings of the Royal Society of Edinburgh, 55, 42–48.
Alias	GLS with structural breaks, break-adjusted GLS, structural change GLS, regime-switching GLS	GLS, Aitken estimator, EGLS, feasible GLS
Apparentées	6	3
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.	Generalized Least Squares (GLS) is a linear regression estimator that extends ordinary least squares to handle situations where the error terms are correlated or have non-constant variance (heteroscedasticity). Introduced by Alexander Craig Aitken in 1935, GLS achieves the Best Linear Unbiased Estimator (BLUE) under a general error covariance structure by weighting observations according to their precision, providing a theoretical bridge between OLS and modern linear mixed models.