Comparer des méthodes

	Régression par Moindres Carrés Ordinaires (MCO)	Moindres Carrés Généralisés Robustes (MCG Robustes)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	2019	1936 / 1980
Auteur d'origine	Wooldridge (textbook treatment); classical least squares	Aitken (GLS theory, 1936); White (robust covariance, 1980)
Type	Linear regression	Robust linear regression
Source fondatrice	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Greene, W. H. (2012). Econometric Analysis (7th ed.). Pearson. Chapter 9: The Generalized Regression Model and Heteroscedasticity. ISBN: 978-0131395381
Alias	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	robust generalized least squares, GLS with robust standard errors, heteroscedasticity-consistent GLS, HC-GLS
Apparentées	5	5
Résumé	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).	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.