Comparer des méthodes

	Erreurs-types robustes à l'hétéroscédasticité (HC)	Erreurs-types robustes aux clusters
Domaine	Statistique	Statistique
Famille	Regression model	Regression model
Année d'origine	1980	1986
Auteur d'origine	Eicker; Huber; White (1980); MacKinnon & White (1985)	Liang & Zeger (GEE sandwich); Cameron & Miller (practitioner synthesis)
Type	Robust covariance estimator for linear regression	Robust variance estimation for regression
Source fondatrice	White, H. (1980). A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity. Econometrica, 48(4), 817-838.	Liang, K. Y. & Zeger, S. L. (1986). Longitudinal Data Analysis Using Generalized Linear Models. Biometrika, 73(1), 13-22.
Alias	robust standard errors, White standard errors, Huber-Eicker-White standard errors, sandwich standard errors	clustered standard errors, cluster-robust inference, clustered variance estimator, Küme Robust Standart Hatalar
Apparentées	5	4
Résumé	Heteroscedasticity-robust standard errors are a correction to the covariance matrix of an OLS regression that yields valid inference when the error variance is not constant. Introduced by Halbert White in 1980 and refined into the finite-sample variants HC1-HC4 by MacKinnon and White in 1985, they leave the coefficient estimates unchanged but rebuild the standard errors so that t and F tests remain trustworthy under heteroscedasticity.	Cluster-robust standard errors correct the variance of regression coefficients when observations are correlated within clusters such as schools, hospitals, or regions. The clustered sandwich estimator grew out of Liang & Zeger's (1986) generalized estimating equations and was synthesized for applied work by Cameron & Miller (2015), delivering valid inference when ordinary standard errors would be too small.