方法对比

	稳健广义最小二乘法 (Robust GLS)	广义最小二乘法 (GLS)	面板广义最小二乘法 (Panel GLS)	稳健OLS（具有稳健标准误的OLS）
领域	计量经济学	统计学	计量经济学	计量经济学
方法族	Regression model	Regression model	Regression model	Regression model
起源年份	1936 / 1980	1935	1935 / developed for panels 1980s–1990s	1980
提出者	Aitken (GLS theory, 1936); White (robust covariance, 1980)	Alexander Craig Aitken	Aitken (1935); extended to panel data by Baltagi and others	Halbert White
类型	Robust linear regression	Linear estimator	Generalized 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	Aitken, A. C. (1935). IV.—On least squares and linear combination of observations. Proceedings of the Royal Society of Edinburgh, 55, 42–48.	Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press. ISBN: 978-0262232586	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	GLS, Aitken estimator, EGLS, feasible GLS	Panel GLS, Generalized Least Squares for panel data, FGLS panel, feasible GLS panel	HC robust regression, White robust OLS, sandwich estimator OLS, OLS with robust standard errors
相关	5	3	3	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.	Generalized Least Squares (GLS) is a linear regression estimator that extends ordinary least squares to handle situations where the error terms are correlated or have non-constant variance (heteroscedasticity). Introduced by Alexander Craig Aitken in 1935, GLS achieves the Best Linear Unbiased Estimator (BLUE) under a general error covariance structure by weighting observations according to their precision, providing a theoretical bridge between OLS and modern linear mixed models.	Panel GLS is a regression method for longitudinal data that explicitly models the non-spherical error structure — heteroscedasticity across units and serial correlation within units — to recover efficient coefficient estimates. Unlike OLS, it weights observations by the inverse of the error covariance matrix, yielding the Best Linear Unbiased Estimator when the error structure is correctly specified.	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.