Comparar métodos

	Errores Estándar Robustos ante Heterocedasticidad (HC)	Errores estándar robustos a clúster
Campo	Estadística	Estadística
Familia	Regression model	Regression model
Año de origen	1980	1986
Autor original	Eicker; Huber; White (1980); MacKinnon & White (1985)	Liang & Zeger (GEE sandwich); Cameron & Miller (practitioner synthesis)
Tipo	Robust covariance estimator for linear regression	Robust variance estimation for regression
Fuente seminal	White, H. (1980). A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity. Econometrica, 48(4), 817-838.	Liang, K. Y. & Zeger, S. L. (1986). Longitudinal Data Analysis Using Generalized Linear Models. Biometrika, 73(1), 13-22.
Alias	robust standard errors, White standard errors, Huber-Eicker-White standard errors, sandwich standard errors	clustered standard errors, cluster-robust inference, clustered variance estimator, Küme Robust Standart Hatalar
Relacionados	5	4
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.	Cluster-robust standard errors correct the variance of regression coefficients when observations are correlated within clusters such as schools, hospitals, or regions. The clustered sandwich estimator grew out of Liang & Zeger's (1986) generalized estimating equations and was synthesized for applied work by Cameron & Miller (2015), delivering valid inference when ordinary standard errors would be too small.