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| Фурие-АРМА модел× | Модел на Фуриеров векторна авторегресия (VAR)× | |
|---|---|---|
| Област | Иконометрия | Иконометрия |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 2004–2006 | 2010s |
| Създател≠ | Becker, Enders, and Hurn | Enders & Lee; extended by Nazlioglu and others to VAR systems |
| Тип≠ | Time series model with smooth structural change | Multivariate time-series model |
| Основополагащ източник≠ | Becker, R., Enders, W., & Hurn, S. (2006). A general test for time dependence in parameters. Journal of Applied Econometrics, 21(7), 1005–1028. link ↗ | 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 ↗ |
| Други названия | Fourier ARMA, ARMA with Fourier terms, trigonometric ARMA, smooth structural change ARMA | Fourier VAR, smooth structural break VAR, trigonometric VAR, Fourier-augmented VAR |
| Свързани≠ | 5 | 6 |
| Резюме≠ | The Fourier ARMA model augments the classical Autoregressive Moving Average framework with low-frequency Fourier (sine and cosine) terms to capture smooth, gradual shifts in the mean or trend of a time series. Unlike dummy-variable approaches, it requires no prior knowledge of when structural change occurred, approximating change with flexible trigonometric functions. | 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. |
| ScholarGateНабор от данни ↗ |
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