Comparer des méthodes

	OLS robuste (OLS avec erreurs-types robustes)	Moindres Carrés Généralisés Robustes (MCG Robustes)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1980	1936 / 1980
Auteur d'origine	Halbert White	Aitken (GLS theory, 1936); White (robust covariance, 1980)
Type	Linear regression with robust inference	Robust linear regression
Source fondatrice	White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4), 817–838.	Greene, W. H. (2012). Econometric Analysis (7th ed.). Pearson. Chapter 9: The Generalized Regression Model and Heteroscedasticity. ISBN: 978-0131395381
Alias	HC robust regression, White robust OLS, sandwich estimator OLS, OLS with robust standard errors	robust generalized least squares, GLS with robust standard errors, heteroscedasticity-consistent GLS, HC-GLS
Apparentées	6	5
Résumé	Robust OLS applies ordinary least squares to estimate coefficients and then replaces the classical standard errors with heteroscedasticity-consistent (HC) standard errors — commonly called White standard errors. This leaves the point estimates unchanged while yielding valid t-statistics and confidence intervals even when the error variance is not constant across observations.	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.