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| Regressió per Mínims Quadrats Ordinàris (MQO)× | Mínims Quadrats Generalitzats Robuts (GLS Robu)× | |
|---|---|---|
| Camp | Econometria | Econometria |
| Família | Regression model | Regression model |
| Any d'origen≠ | 2019 | 1936 / 1980 |
| Autor original≠ | Wooldridge (textbook treatment); classical least squares | Aitken (GLS theory, 1936); White (robust covariance, 1980) |
| Tipus≠ | Linear regression | Robust linear regression |
| Font seminal≠ | 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 |
| Àlies | 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 |
| Relacionats | 5 | 5 |
| Resum≠ | 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. |
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