Módszerek összehasonlítása

	Robusztus általánosított legkisebb négyzetek (Robust GLS)	Általánosított legkisebb négyzetek (GLS)	Regresszió Ordináris Legkisebb Négyzetes (OLS) módszerrel
Tudományterület	Ökonometria	Statisztika	Ökonometria
Módszercsalád	Regression model	Regression model	Regression model
Keletkezés éve	1936 / 1980	1935	2019
Megalkotó	Aitken (GLS theory, 1936); White (robust covariance, 1980)	Alexander Craig Aitken	Wooldridge (textbook treatment); classical least squares
Típus	Robust linear regression	Linear estimator	Linear regression
Alapmű	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
Alternatív nevek	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
Kapcsolódó	5	3	5
Összefoglaló	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).