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Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| WLS de Fourier (Weighted Least Squares Flexible de Fourier)× | Régression par Moindres Carrés Ordinaires (MCO)× | |
|---|---|---|
| Domaine | Économétrie | Économétrie |
| Famille | Regression model | Regression model |
| Année d'origine≠ | 2012 (Fourier WLS application); 1984 (Fourier flexible form) | 2019 |
| Auteur d'origine≠ | Enders & Lee (2012); Gallant (1984) for the Fourier flexible form | Wooldridge (textbook treatment); classical least squares |
| Type≠ | Nonlinear time-series regression | Linear regression |
| Source fondatrice≠ | Enders, W., & Lee, J. (2012). A unit root test using a Fourier series to approximate smooth breaks. Oxford Bulletin of Economics and Statistics, 74(4), 574–599. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Alias | Fourier WLS, Fourier-weighted least squares, smooth break WLS, Fourier flexible regression | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Apparentées≠ | 1 | 5 |
| Résumé≠ | Fourier WLS is a time-series regression technique that embeds low-frequency Fourier trigonometric terms into a Weighted Least Squares framework to capture smooth, gradual structural breaks in means or trends without requiring the researcher to pre-specify their location, timing, or number. | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). |
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