Bandingkan kaedah

	Regresi Kuasa Dua Terkecil Biasa (OLS)	Kuadrat Terkecil Umum Teguh (Robust GLS)	OLS Teguh (OLS dengan Ralat Piawai Teguh)
Bidang	Ekonometrik	Ekonometrik	Ekonometrik
Keluarga	Regression model	Regression model	Regression model
Tahun asal	2019	1936 / 1980	1980
Pengasas	Wooldridge (textbook treatment); classical least squares	Aitken (GLS theory, 1936); White (robust covariance, 1980)	Halbert White
Jenis	Linear regression	Robust linear regression	Linear regression with robust inference
Sumber perintis	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Greene, W. H. (2012). Econometric Analysis (7th ed.). Pearson. Chapter 9: The Generalized Regression Model and Heteroscedasticity. ISBN: 978-0131395381	White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4), 817–838.
Alias	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	robust generalized least squares, GLS with robust standard errors, heteroscedasticity-consistent GLS, HC-GLS	HC robust regression, White robust OLS, sandwich estimator OLS, OLS with robust standard errors
Berkaitan	5	5	6
Ringkasan	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 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.	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.