Comparer des méthodes

	Test de White pour l'hétéroscédasticité	Régression par Moindres Carrés Ordinaires (MCO)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1980	2019
Auteur d'origine	Halbert White	Wooldridge (textbook treatment); classical least squares
Type	General test for heteroskedasticity	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	White's general heteroskedasticity test, White değişen varyans testi	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Apparentées	3	5
Résumé	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.	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).