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| Structural Break WLS× | Mínims Quadrats Ponderats Robuts (Robust WLS)× | |
|---|---|---|
| Camp | Econometria | Econometria |
| Família | Regression model | Regression model |
| Any d'origen≠ | 1998 (break framework); WLS long-established | 1964/1981 |
| Autor original≠ | Bai & Perron (structural break framework); WLS classical | Huber, P. J. |
| Tipus≠ | Weighted regression with regime shifts | Robust weighted regression |
| Font seminal≠ | Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47-78. DOI ↗ | Huber, P. J. (1981). Robust Statistics. Wiley. ISBN: 978-0471418054 |
| Àlies | WLS with structural change, break-corrected WLS, segmented WLS, structural break weighted regression | robust weighted least squares, RWLS, heteroscedasticity-robust WLS, outlier-robust weighted regression |
| Relacionats | 5 | 5 |
| Resum≠ | Structural Break WLS combines Weighted Least Squares estimation with explicit detection and correction for structural breaks — abrupt regime shifts — in the data. By identifying break points and assigning observation-level weights that account for heteroscedasticity within and across regimes, the estimator delivers consistent, efficient coefficient estimates even when the error variance changes dramatically at a break. | 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. |
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