Compară metode

	Metoda celor mai mici pătrate generalizate (GLS)	Metoda de regresie cu cele mai mici pătrate generalizate pe panel (Panel GLS)	OLS Robust (OLS cu erori standard robuste)
Domeniu	Statistică	Econometrie	Econometrie
Familie	Regression model	Regression model	Regression model
Anul apariției	1935	1935 / developed for panels 1980s–1990s	1980
Autorul original	Alexander Craig Aitken	Aitken (1935); extended to panel data by Baltagi and others	Halbert White
Tip	Linear estimator	Generalized linear regression	Linear regression with robust inference
Sursa seminală	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. (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	GLS, Aitken estimator, EGLS, feasible 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	3	3	6
Rezumat	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.	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.