Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| GLS ar strukturālām pārtraukuma vietām× | Vispārīgais mazāko kvadrātu metodes (GLS) novērtētājs× | |
|---|---|---|
| Nozare≠ | Ekonometrija | Statistika |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1998 (structural break GLS formalization) | 1935 |
| Autors≠ | Bai & Perron (1998); GLS framework by Aitken (1936) | Alexander Craig Aitken |
| Tips≠ | Regression estimator | Linear estimator |
| Pirmavots≠ | 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 ↗ |
| Citi nosaukumi≠ | GLS with structural breaks, break-adjusted GLS, structural change GLS, regime-switching GLS | GLS, Aitken estimator, EGLS, feasible GLS |
| Saistītās≠ | 6 | 3 |
| Kopsavilkums≠ | 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. | Generalized Least Squares (GLS) is a linear regression estimator that extends ordinary least squares to handle situations where the error terms are correlated or have non-constant variance (heteroscedasticity). Introduced by Alexander Craig Aitken in 1935, GLS achieves the Best Linear Unbiased Estimator (BLUE) under a general error covariance structure by weighting observations according to their precision, providing a theoretical bridge between OLS and modern linear mixed models. |
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