Comparar métodos

	Errores Estándar Robustos ante Heterocedasticidad (HC)	Mínimos Cuadrados Ponderados (WLS)
Campo	Estadística	Estadística
Familia	Regression model	Regression model
Año de origen	1980	1935
Autor original	Eicker; Huber; White (1980); MacKinnon & White (1985)	Alexander Craig Aitken
Tipo	Robust covariance estimator for linear regression	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	robust standard errors, White standard errors, Huber-Eicker-White standard errors, sandwich standard errors	WLS, weighted regression, heteroscedasticity-corrected OLS, variance-weighted least squares
Relacionados	5	3
Resumen	Heteroscedasticity-robust standard errors are a correction to the covariance matrix of an OLS regression that yields valid inference when the error variance is not constant. Introduced by Halbert White in 1980 and refined into the finite-sample variants HC1-HC4 by MacKinnon and White in 1985, they leave the coefficient estimates unchanged but rebuild the standard errors so that t and F tests remain trustworthy under heteroscedasticity.	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.