Comparer des méthodes

	OLS robuste (OLS avec erreurs-types robustes)	Régression quantile
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1980	1978
Auteur d'origine	Halbert White	Koenker & Bassett
Type	Linear regression with robust inference	Conditional quantile regression
Source fondatrice	White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4), 817–838.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
Alias	HC robust regression, White robust OLS, sandwich estimator OLS, OLS with robust standard errors	conditional quantile regression, regression quantiles, Kantil Regresyon
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.	Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails.