Comparar métodos

	Mínimos Cuadrados Generalizados (GLS)	OLS robusta (OLS con errores estándar robustos)
Campo	Estadística	Econometría
Familia	Regression model	Regression model
Año de origen	1935	1980
Autor original	Alexander Craig Aitken	Halbert White
Tipo	Linear estimator	Linear regression with robust inference
Fuente seminal	Aitken, A. C. (1935). IV.—On least squares and linear combination of observations. Proceedings of the Royal Society of Edinburgh, 55, 42–48.	White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4), 817–838.
Alias	GLS, Aitken estimator, EGLS, feasible GLS	HC robust regression, White robust OLS, sandwich estimator OLS, OLS with robust standard errors
Relacionados	3	6
Resumen	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.	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.