Comparer des méthodes

	Régression par Moindres Carrés Ordinaires (MCO)	Test de White pour l'hétéroscédasticité
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	2019	1980
Auteur d'origine	Wooldridge (textbook treatment); classical least squares	Halbert White
Type	Linear regression	General test for heteroskedasticity
Source fondatrice	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4), 817–838.
Alias	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	White's general heteroskedasticity test, White değişen varyans testi
Apparentées	5	3
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).	The White test, introduced by Halbert White in 1980, is a general test for heteroskedasticity that makes no assumption about its functional form. It regresses the squared OLS residuals on the regressors, their squares, and their cross-products, so it can detect heteroskedasticity related to any of these terms. The same 1980 paper introduced the heteroskedasticity-consistent ('White') standard errors that are the standard remedy when the test rejects.