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| GLS de Fourier (Moindres Carrés Généralisés de Fourier)× | Moindres Carrés Généralisés (MCG)× | |
|---|---|---|
| Domaine≠ | Économétrie | Statistique |
| Famille | Regression model | Regression model |
| Année d'origine≠ | 2004-2012 | 1935 |
| Auteur d'origine≠ | Becker, Enders, and Hurn; extended by Enders and Lee | Alexander Craig Aitken |
| Type≠ | Time-series regression estimator | Linear estimator |
| Source fondatrice≠ | Becker, R., Enders, W., & Hurn, S. (2004). A general test for time dependence in parameters. Journal of Applied Econometrics, 19(7), 899-906. 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 ↗ |
| Alias≠ | Fourier GLS, Fourier-based GLS, Fourier flexible GLS, spectral GLS | GLS, Aitken estimator, EGLS, feasible GLS |
| Apparentées≠ | 1 | 3 |
| Résumé≠ | Fourier GLS embeds low-frequency trigonometric (Fourier) terms into a generalized least squares framework to capture smooth, gradual structural change in a time series without requiring the researcher to specify when or how many breaks occurred. The approach is particularly valued in unit root testing and cointegration analysis where conventional break-date assumptions may be arbitrary. | 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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