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	LPM (OLS z odpornymi estymatorami odchylenia standardowego)	Metoda najmniejszych kwadratów uogólnionych (GLS)
Dziedzina	Ekonometria	Statystyka
Rodzina	Regression model	Regression model
Rok powstania	1980	1935
Twórca	Halbert White	Alexander Craig Aitken
Typ	Linear regression with robust inference	Linear estimator
Źródło pierwotne	White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4), 817–838.	Aitken, A. C. (1935). IV.—On least squares and linear combination of observations. Proceedings of the Royal Society of Edinburgh, 55, 42–48.
Inne nazwy	HC robust regression, White robust OLS, sandwich estimator OLS, OLS with robust standard errors	GLS, Aitken estimator, EGLS, feasible GLS
Pokrewne	6	3
Podsumowanie	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.	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.