Comparar métodos

	OLS robusta (OLS con errores estándar robustos)	Mínimos Cuadrados Ponderados (WLS)
Campo	Econometría	Estadística
Familia	Regression model	Regression model
Año de origen	1980	1935
Autor original	Halbert White	Alexander Craig Aitken
Tipo	Linear regression with robust inference	Weighted linear estimator
Fuente seminal	White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4), 817–838.	Aitken, A. C. (1935). IV.—On least squares and linear combination of observations. Proceedings of the Royal Society of Edinburgh, 55, 42–48.
Alias	HC robust regression, White robust OLS, sandwich estimator OLS, OLS with robust standard errors	WLS, weighted regression, heteroscedasticity-corrected OLS, variance-weighted least squares
Relacionados	6	3
Resumen	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.	Weighted Least Squares is a generalization of Ordinary Least Squares (OLS) regression that assigns each observation a weight inversely proportional to its error variance, thereby down-weighting high-variance data points and up-weighting precise ones. Introduced in its general matrix form by Alexander Craig Aitken in 1935, WLS is the canonical remedy when heteroscedasticity is present and the error variance structure is known or can be reliably estimated.