Сравнение на методи

	Robust OLS (OLS с робастни стандартни грешки)	Робастни обобщени най-малки квадрати (Robust GLS)
Област	Иконометрия	Иконометрия
Семейство	Regression model	Regression model
Година на възникване	1980	1936 / 1980
Създател	Halbert White	Aitken (GLS theory, 1936); White (robust covariance, 1980)
Тип	Linear regression with robust inference	Robust linear regression
Основополагащ източник	White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4), 817–838.	Greene, W. H. (2012). Econometric Analysis (7th ed.). Pearson. Chapter 9: The Generalized Regression Model and Heteroscedasticity. ISBN: 978-0131395381
Други названия	HC robust regression, White robust OLS, sandwich estimator OLS, OLS with robust standard errors	robust generalized least squares, GLS with robust standard errors, heteroscedasticity-consistent GLS, HC-GLS
Свързани	6	5
Резюме	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.	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.