Usporedite metode

	Robusni ponderirani najmanji kvadrati (Robust WLS)	Kvantilna regresija	Robusni generalizirani najmanji kvadrati (Robust GLS)	Robustni OLS (OLS s robustnim standardnim pogreškama)
Područje	Ekonometrija	Ekonometrija	Ekonometrija	Ekonometrija
Obitelj	Regression model	Regression model	Regression model	Regression model
Godina nastanka	1964/1981	1978	1936 / 1980	1980
Tvorac	Huber, P. J.	Koenker & Bassett	Aitken (GLS theory, 1936); White (robust covariance, 1980)	Halbert White
Vrsta	Robust weighted regression	Conditional quantile regression	Robust linear regression	Linear regression with robust inference
Temeljni izvor	Huber, P. J. (1981). Robust Statistics. Wiley. ISBN: 978-0471418054	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.	Greene, W. H. (2012). Econometric Analysis (7th ed.). Pearson. Chapter 9: The Generalized Regression Model and Heteroscedasticity. ISBN: 978-0131395381	White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4), 817–838.
Drugi nazivi	robust weighted least squares, RWLS, heteroscedasticity-robust WLS, outlier-robust weighted regression	conditional quantile regression, regression quantiles, Kantil Regresyon	robust generalized least squares, GLS with robust standard errors, heteroscedasticity-consistent GLS, HC-GLS	HC robust regression, White robust OLS, sandwich estimator OLS, OLS with robust standard errors
Srodne	5	5	5	6
Sažetak	Robust WLS combines weighted least squares — which corrects for known or estimated heteroscedasticity — with robust M-estimation that down-weights influential outliers. The result is a regression estimator that is simultaneously efficient under non-constant error variance and resistant to observations that would otherwise distort coefficient estimates.	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 GLS extends classical Generalized Least Squares by pairing GLS coefficient estimation with heteroscedasticity- and autocorrelation-consistent (HAC) standard errors, or by using M-estimation within the GLS framework. It corrects for non-spherical errors — heteroscedasticity, autocorrelation, or both — while also guarding inference against misspecification of the error covariance structure.	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.