Jämför metoder
Granska de valda metoderna sida vid sida; rader som skiljer sig är markerade.
| Robust viktad minsta kvadrat-metod (Robust WLS)× | Robust Generaliserad Minsta Kvadrat (Robust GLS)× | Robust OLS (OLS med robusta standardfel)× | |
|---|---|---|---|
| Ämnesområde | Ekonometri | Ekonometri | Ekonometri |
| Familj | Regression model | Regression model | Regression model |
| Ursprungsår≠ | 1964/1981 | 1936 / 1980 | 1980 |
| Upphovsperson≠ | Huber, P. J. | Aitken (GLS theory, 1936); White (robust covariance, 1980) | Halbert White |
| Typ≠ | Robust weighted regression | Robust linear regression | Linear regression with robust inference |
| Ursprungskälla≠ | Huber, P. J. (1981). Robust Statistics. Wiley. ISBN: 978-0471418054 | 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. DOI ↗ |
| Alias | robust weighted least squares, RWLS, heteroscedasticity-robust WLS, outlier-robust weighted regression | 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 |
| Närliggande≠ | 5 | 5 | 6 |
| Sammanfattning≠ | Robust WLS combines weighted least squares — which corrects for known or estimated heteroscedasticity — with robust M-estimation that down-weights influential outliers. The result is a regression estimator that is simultaneously efficient under non-constant error variance and resistant to observations that would otherwise distort coefficient estimates. | 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. |
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