Comparer des méthodes

	Régression quantile	OLS robuste (OLS avec erreurs-types robustes)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1978	1980
Auteur d'origine	Koenker & Bassett	Halbert White
Type	Conditional quantile regression	Linear regression with robust inference
Source fondatrice	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.	White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4), 817–838.
Alias	conditional quantile regression, regression quantiles, Kantil Regresyon	HC robust regression, White robust OLS, sandwich estimator OLS, OLS with robust standard errors
Apparentées	5	6
Résumé	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.	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.