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| GLS con Rottura Strutturale× | Robust Generalized Least Squares (Robust GLS)× | |
|---|---|---|
| Campo | Econometria | Econometria |
| Famiglia | Regression model | Regression model |
| Anno di origine≠ | 1998 (structural break GLS formalization) | 1936 / 1980 |
| Ideatore≠ | Bai & Perron (1998); GLS framework by Aitken (1936) | Aitken (GLS theory, 1936); White (robust covariance, 1980) |
| Tipo≠ | Regression estimator | Robust linear regression |
| Fonte seminale≠ | Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47–78. DOI ↗ | Greene, W. H. (2012). Econometric Analysis (7th ed.). Pearson. Chapter 9: The Generalized Regression Model and Heteroscedasticity. ISBN: 978-0131395381 |
| Alias | GLS with structural breaks, break-adjusted GLS, structural change GLS, regime-switching GLS | robust generalized least squares, GLS with robust standard errors, heteroscedasticity-consistent GLS, HC-GLS |
| Correlati≠ | 6 | 5 |
| Sintesi≠ | Structural Break GLS combines Generalized Least Squares estimation with explicit allowance for regime shifts in the data-generating process. The method estimates separate coefficient vectors for each segment defined by detected break dates while correcting for non-spherical errors — heteroscedasticity or autocorrelation — that frequently accompany structural change, yielding consistent and efficient estimates across all regimes. | 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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