方法对比

	异方差稳健 (HC) 标准误	加权最小二乘法 (WLS)
领域	统计学	统计学
方法族	Regression model	Regression model
起源年份	1980	1935
提出者	Eicker; Huber; White (1980); MacKinnon & White (1985)	Alexander Craig Aitken
类型	Robust covariance estimator for linear regression	Weighted linear estimator
开创性文献	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.
别名	robust standard errors, White standard errors, Huber-Eicker-White standard errors, sandwich standard errors	WLS, weighted regression, heteroscedasticity-corrected OLS, variance-weighted least squares
相关	5	3
摘要	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.