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| Modèle VAR de Fourier× | Modèle à Correction d'Erreur Vectoriel de Fourier (Fourier VECM)× | |
|---|---|---|
| Domaine | Économétrie | Économétrie |
| Famille | Regression model | Regression model |
| Année d'origine≠ | 2010s | 2004–2012 |
| Auteur d'origine≠ | Enders & Lee; extended by Nazlioglu and others to VAR systems | Enders & Lee (2004/2012); extended to VECM by subsequent authors |
| Type≠ | Multivariate time-series model | Error-correction model with Fourier terms |
| 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 ↗ | 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 ↗ |
| Alias | Fourier VAR, smooth structural break VAR, trigonometric VAR, Fourier-augmented VAR | Fourier VECM, Fourier-approximation VECM, smooth-break VECM, trigonometric VECM |
| Apparentées≠ | 6 | 5 |
| Résumé≠ | The Fourier VAR model extends the standard Vector Autoregression by replacing fixed deterministic terms with Fourier trigonometric components, allowing the intercept (and optionally the trend) to shift gradually and smoothly over time. This eliminates the need to pre-specify the number, timing, or shape of structural breaks in a multivariate time-series system. | The Fourier VECM augments the classical vector error correction model with low-frequency trigonometric terms — sine and cosine components — to capture smooth, gradual structural change in cointegrating relationships without specifying the number or timing of breaks in advance. It is used for multivariate cointegrated systems where long-run equilibria may shift gradually over time. |
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