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| Structural Break WLS× | Mínims Quadrats Ponderats (WLS)× | |
|---|---|---|
| Camp≠ | Econometria | Estadística |
| Família | Regression model | Regression model |
| Any d'origen≠ | 1998 (break framework); WLS long-established | 1935 |
| Autor original≠ | Bai & Perron (structural break framework); WLS classical | Alexander Craig Aitken |
| Tipus≠ | Weighted regression with regime shifts | Weighted linear estimator |
| Font seminal≠ | Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47-78. DOI ↗ | Aitken, A. C. (1935). IV.—On least squares and linear combination of observations. Proceedings of the Royal Society of Edinburgh, 55, 42–48. DOI ↗ |
| Àlies | WLS with structural change, break-corrected WLS, segmented WLS, structural break weighted regression | WLS, weighted regression, heteroscedasticity-corrected OLS, variance-weighted least squares |
| Relacionats≠ | 5 | 3 |
| 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. | Weighted Least Squares is a generalization of Ordinary Least Squares (OLS) regression that assigns each observation a weight inversely proportional to its error variance, thereby down-weighting high-variance data points and up-weighting precise ones. Introduced in its general matrix form by Alexander Craig Aitken in 1935, WLS is the canonical remedy when heteroscedasticity is present and the error variance structure is known or can be reliably estimated. |
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