Comparer des méthodes

	Régression par Moindres Carrés Ordinaires (MCO)	Régression quantile
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	2019	1978
Auteur d'origine	Wooldridge (textbook treatment); classical least squares	Koenker & Bassett
Type	Linear regression	Conditional quantile regression
Source fondatrice	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
Alias	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	conditional quantile regression, regression quantiles, Kantil Regresyon
Apparentées	5	5
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).	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.