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| Robust Generaliserad Minsta Kvadrat (Robust GLS)× | Vanligaste minsta kvadratmetoden (OLS) Regression× | |
|---|---|---|
| Ämnesområde | Ekonometri | Ekonometri |
| Familj | Regression model | Regression model |
| Ursprungsår≠ | 1936 / 1980 | 2019 |
| Upphovsperson≠ | Aitken (GLS theory, 1936); White (robust covariance, 1980) | Wooldridge (textbook treatment); classical least squares |
| Typ≠ | Robust linear regression | Linear regression |
| Ursprungskälla≠ | 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 |
| 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 |
| Närliggande | 5 | 5 |
| Sammanfattning≠ | 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). |
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