Comparar métodos

	Errores Estándar Robustos ante Heterocedasticidad (HC)	Bootstrap salvaje para inferencia en regresión
Campo	Estadística	Estadística
Familia	Regression model	Regression model
Año de origen	1980	1986
Autor original	Eicker; Huber; White (1980); MacKinnon & White (1985)	Wu (1986); refined by Davidson & Flachaire (2008)
Tipo	Robust covariance estimator for linear regression	Resampling-based regression inference
Fuente seminal	White, H. (1980). A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity. Econometrica, 48(4), 817-838.	Wu, C. F. J. (1986). Jackknife, Bootstrap and Other Resampling Methods in Regression Analysis. Annals of Statistics, 14(4), 1261-1295.
Alias	robust standard errors, White standard errors, Huber-Eicker-White standard errors, sandwich standard errors	wild bootstrap, wild cluster bootstrap, Wu-Liu resampling, Wild Bootstrap
Relacionados	5	5
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.	The wild bootstrap is a resampling method for regression models with heteroscedastic errors, introduced by Wu (1986) and refined by Davidson and Flachaire (2008). It builds a bootstrap distribution by rescaling each fitted residual with a random sign, so that standard errors and confidence intervals stay valid when the error variance is not constant or the data are clustered.