Bandingkan kaedah

	Kuadrat Terkecil Umum Teguh (Robust GLS)	Regresi Kuasa Dua Terkecil Biasa (OLS)	Panel Generalized Least Squares (Panel GLS)
Bidang	Ekonometrik	Ekonometrik	Ekonometrik
Keluarga	Regression model	Regression model	Regression model
Tahun asal	1936 / 1980	2019	1935 / developed for panels 1980s–1990s
Pengasas	Aitken (GLS theory, 1936); White (robust covariance, 1980)	Wooldridge (textbook treatment); classical least squares	Aitken (1935); extended to panel data by Baltagi and others
Jenis	Robust linear regression	Linear regression	Generalized linear regression
Sumber perintis	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	Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press. ISBN: 978-0262232586
Alias	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	Panel GLS, Generalized Least Squares for panel data, FGLS panel, feasible GLS panel
Berkaitan	5	5	3
Ringkasan	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).	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.