手法を比較

	ロバスト一般化最小二乗法 (Robust GLS)	一般化最小二乗法 (GLS)	パネル一般化最小二乗法（Panel GLS）
分野	計量経済学	統計学	計量経済学
系統	Regression model	Regression model	Regression model
提唱年	1936 / 1980	1935	1935 / developed for panels 1980s–1990s
提唱者	Aitken (GLS theory, 1936); White (robust covariance, 1980)	Alexander Craig Aitken	Aitken (1935); extended to panel data by Baltagi and others
種類	Robust linear regression	Linear estimator	Generalized linear regression
原典	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
別名	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
関連	5	3	3
概要	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.