Comparer des méthodes

	Erreurs-types robustes à l'hétéroscédasticité (HC)	Régression par Moindres Carrés Ordinaires (MCO)
Domaine	Statistique	Économétrie
Famille	Regression model	Regression model
Année d'origine	1980	2019
Auteur d'origine	Eicker; Huber; White (1980); MacKinnon & White (1985)	Wooldridge (textbook treatment); classical least squares
Type	Robust covariance estimator for linear regression	Linear regression
Source fondatrice	White, H. (1980). A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity. Econometrica, 48(4), 817-838.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Alias	robust standard errors, White standard errors, Huber-Eicker-White standard errors, sandwich standard errors	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Apparentées	5	5
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.	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).