Compara mètodes

	Errors estàndard robustos (HC) davant l'heteroscedasticitat	Errors estàndard robustes a clústers
Camp	Estadística	Estadística
Família	Regression model	Regression model
Any d'origen	1980	1986
Autor original	Eicker; Huber; White (1980); MacKinnon & White (1985)	Liang & Zeger (GEE sandwich); Cameron & Miller (practitioner synthesis)
Tipus	Robust covariance estimator for linear regression	Robust variance estimation for regression
Font 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.
Àlies	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
Relacionats	5	4
Resum	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.