Compara mètodes

	Mínims Quadrats Generalitzats Robuts (GLS Robu)	Regressió per Mínims Quadrats Ordinàris (MQO)	MCO robusta (MCO amb errors estàndard robustos)
Camp	Econometria	Econometria	Econometria
Família	Regression model	Regression model	Regression model
Any d'origen	1936 / 1980	2019	1980
Autor original	Aitken (GLS theory, 1936); White (robust covariance, 1980)	Wooldridge (textbook treatment); classical least squares	Halbert White
Tipus	Robust linear regression	Linear regression	Linear regression with robust inference
Font seminal	Greene, W. H. (2012). Econometric Analysis (7th ed.). Pearson. Chapter 9: The Generalized Regression Model and Heteroscedasticity. ISBN: 978-0131395381	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4), 817–838.
Àlies	robust generalized least squares, GLS with robust standard errors, heteroscedasticity-consistent GLS, HC-GLS	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	HC robust regression, White robust OLS, sandwich estimator OLS, OLS with robust standard errors
Relacionats	5	5	6
Resum	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.	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).	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.