Порівняння методів

	Robust Generalized Least Squares (Robust GLS)	Зважені найменші квадрати (ЗНК)
Галузь	Економетрика	Статистика
Родина	Regression model	Regression model
Рік появи	1936 / 1980	1935
Автор методу	Aitken (GLS theory, 1936); White (robust covariance, 1980)	Alexander Craig Aitken
Тип	Robust linear regression	Weighted linear estimator
Основоположне джерело	Greene, W. H. (2012). Econometric Analysis (7th ed.). Pearson. Chapter 9: The Generalized Regression Model and Heteroscedasticity. ISBN: 978-0131395381	Aitken, A. C. (1935). IV.—On least squares and linear combination of observations. Proceedings of the Royal Society of Edinburgh, 55, 42–48.
Інші назви	robust generalized least squares, GLS with robust standard errors, heteroscedasticity-consistent GLS, HC-GLS	WLS, weighted regression, heteroscedasticity-corrected OLS, variance-weighted least squares
Пов'язані	5	3
Підсумок	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.	Weighted Least Squares is a generalization of Ordinary Least Squares (OLS) regression that assigns each observation a weight inversely proportional to its error variance, thereby down-weighting high-variance data points and up-weighting precise ones. Introduced in its general matrix form by Alexander Craig Aitken in 1935, WLS is the canonical remedy when heteroscedasticity is present and the error variance structure is known or can be reliably estimated.