Compară metode

	Regresie Liniară Generalizată Robustă (Robust GLS)	Metoda celor mai mici pătrate generalizate (GLS)	Regresia prin metoda celor mai mici pătrate ordinare (OLS)
Domeniu	Econometrie	Statistică	Econometrie
Familie	Regression model	Regression model	Regression model
Anul apariției	1936 / 1980	1935	2019
Autorul original	Aitken (GLS theory, 1936); White (robust covariance, 1980)	Alexander Craig Aitken	Wooldridge (textbook treatment); classical least squares
Tip	Robust linear regression	Linear estimator	Linear regression
Sursa seminală	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.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Denumiri alternative	robust generalized least squares, GLS with robust standard errors, heteroscedasticity-consistent GLS, HC-GLS	GLS, Aitken estimator, EGLS, feasible GLS	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Înrudite	5	3	5
Rezumat	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.	Generalized Least Squares (GLS) is a linear regression estimator that extends ordinary least squares to handle situations where the error terms are correlated or have non-constant variance (heteroscedasticity). Introduced by Alexander Craig Aitken in 1935, GLS achieves the Best Linear Unbiased Estimator (BLUE) under a general error covariance structure by weighting observations according to their precision, providing a theoretical bridge between OLS and modern linear mixed models.	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).