方法对比

	稳健广义最小二乘法 (Robust GLS)	普通最小二乘法 (OLS) 回归	稳健OLS（具有稳健标准误的OLS）
领域	计量经济学	计量经济学	计量经济学
方法族	Regression model	Regression model	Regression model
起源年份	1936 / 1980	2019	1980
提出者	Aitken (GLS theory, 1936); White (robust covariance, 1980)	Wooldridge (textbook treatment); classical least squares	Halbert White
类型	Robust linear regression	Linear regression	Linear regression with robust inference
开创性文献	Greene, W. H. (2012). Econometric Analysis (7th ed.). Pearson. Chapter 9: The Generalized Regression Model and Heteroscedasticity. ISBN: 978-0131395381	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4), 817–838.
别名	robust generalized least squares, GLS with robust standard errors, heteroscedasticity-consistent GLS, HC-GLS	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	HC robust regression, White robust OLS, sandwich estimator OLS, OLS with robust standard errors
相关	5	5	6
摘要	Robust GLS extends classical Generalized Least Squares by pairing GLS coefficient estimation with heteroscedasticity- and autocorrelation-consistent (HAC) standard errors, or by using M-estimation within the GLS framework. It corrects for non-spherical errors — heteroscedasticity, autocorrelation, or both — while also guarding inference against misspecification of the error covariance structure.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).	Robust OLS applies ordinary least squares to estimate coefficients and then replaces the classical standard errors with heteroscedasticity-consistent (HC) standard errors — commonly called White standard errors. This leaves the point estimates unchanged while yielding valid t-statistics and confidence intervals even when the error variance is not constant across observations.