Comparer des méthodes

	Régression quantile	Régression par Moindres Carrés Ordinaires (MCO)	Modèle à effets fixes pour données de panel
Domaine	Économétrie	Économétrie	Économétrie
Famille	Regression model	Regression model	Regression model
Année d'origine	1978	2019	2014
Auteur d'origine	Koenker & Bassett	Wooldridge (textbook treatment); classical least squares	Hsiao (textbook treatment); within transformation of panel data
Type	Conditional quantile regression	Linear regression	Panel data regression
Source fondatrice	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press.
Alias	conditional quantile regression, regression quantiles, Kantil Regresyon	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli
Apparentées	5	5	5
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.	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 Panel Data Fixed Effects model estimates relationships from panel data (the same units observed over several time periods) while controlling for unit- and/or time-specific effects, supporting causal inference. It is developed as the within estimator in standard treatments such as Hsiao's Analysis of Panel Data (2014).