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| Uogólniona metoda najmniejszych kwadratów (Robust GLS)× | Ważone Metody Najmniejszych Kwadratów (WLS)× | |
|---|---|---|
| Dziedzina≠ | Ekonometria | Statystyka |
| Rodzina | Regression model | Regression model |
| Rok powstania≠ | 1936 / 1980 | 1935 |
| Twórca≠ | Aitken (GLS theory, 1936); White (robust covariance, 1980) | Alexander Craig Aitken |
| Typ≠ | Robust linear regression | Weighted linear estimator |
| Źródło pierwotne≠ | 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. DOI ↗ |
| Inne nazwy | robust generalized least squares, GLS with robust standard errors, heteroscedasticity-consistent GLS, HC-GLS | WLS, weighted regression, heteroscedasticity-corrected OLS, variance-weighted least squares |
| Pokrewne≠ | 5 | 3 |
| Podsumowanie≠ | 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. | Weighted Least Squares is a generalization of Ordinary Least Squares (OLS) regression that assigns each observation a weight inversely proportional to its error variance, thereby down-weighting high-variance data points and up-weighting precise ones. Introduced in its general matrix form by Alexander Craig Aitken in 1935, WLS is the canonical remedy when heteroscedasticity is present and the error variance structure is known or can be reliably estimated. |
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