Usporedite metode

	Robusni ponderirani najmanji kvadrati (Robust WLS)	Robusni generalizirani najmanji kvadrati (Robust GLS)	Robustni OLS (OLS s robustnim standardnim pogreškama)
Područje	Ekonometrija	Ekonometrija	Ekonometrija
Obitelj	Regression model	Regression model	Regression model
Godina nastanka	1964/1981	1936 / 1980	1980
Tvorac	Huber, P. J.	Aitken (GLS theory, 1936); White (robust covariance, 1980)	Halbert White
Vrsta	Robust weighted regression	Robust linear regression	Linear regression with robust inference
Temeljni izvor	Huber, P. J. (1981). Robust Statistics. Wiley. ISBN: 978-0471418054	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	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	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.	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.