Compară metode

	Regresie Liniară Generalizată Robustă (Robust GLS)	Metoda de regresie cu cele mai mici pătrate generalizate pe panel (Panel GLS)	OLS Robust (OLS cu erori standard robuste)
Domeniu	Econometrie	Econometrie	Econometrie
Familie	Regression model	Regression model	Regression model
Anul apariției	1936 / 1980	1935 / developed for panels 1980s–1990s	1980
Autorul original	Aitken (GLS theory, 1936); White (robust covariance, 1980)	Aitken (1935); extended to panel data by Baltagi and others	Halbert White
Tip	Robust linear regression	Generalized linear regression	Linear regression with robust inference
Sursa seminală	Greene, W. H. (2012). Econometric Analysis (7th ed.). Pearson. Chapter 9: The Generalized Regression Model and Heteroscedasticity. ISBN: 978-0131395381	Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press. ISBN: 978-0262232586	White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4), 817–838.
Denumiri alternative	robust generalized least squares, GLS with robust standard errors, heteroscedasticity-consistent GLS, HC-GLS	Panel GLS, Generalized Least Squares for panel data, FGLS panel, feasible GLS panel	HC robust regression, White robust OLS, sandwich estimator OLS, OLS with robust standard errors
Înrudite	5	3	6
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.	Panel GLS is a regression method for longitudinal data that explicitly models the non-spherical error structure — heteroscedasticity across units and serial correlation within units — to recover efficient coefficient estimates. Unlike OLS, it weights observations by the inverse of the error covariance matrix, yielding the Best Linear Unbiased Estimator when the error structure is correctly specified.	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.