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| Model AR z rozszerzeniem Fouriera× | Model Autoregresywny (AR)× | |
|---|---|---|
| Dziedzina | Ekonometria | Ekonometria |
| Rodzina | Regression model | Regression model |
| Rok powstania≠ | 2012 | 1970s (popularised 1976) |
| Twórca≠ | Enders & Lee | George E. P. Box and Gwilym M. Jenkins |
| Typ≠ | Time series model with Fourier augmentation | Time series model |
| Źródło pierwotne≠ | 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 ↗ | Box, G. E. P., & Jenkins, G. M. (1976). Time Series Analysis: Forecasting and Control (revised ed.). Holden-Day. ISBN: 978-0816211043 |
| Inne nazwy | Fourier AR, trigonometric AR model, smooth transition AR with Fourier terms, FAR model | AR model, AR(p) model, autoregression, AR process |
| Pokrewne | 6 | 6 |
| Podsumowanie≠ | The Fourier AR model extends the standard autoregressive specification by adding trigonometric (sine and cosine) terms to the deterministic component. This allows the model to capture smooth, gradual shifts in the mean or trend of a time series without requiring the researcher to locate or count structural break points explicitly. | An autoregressive model of order p — AR(p) — expresses the current value of a time series as a linear function of its own p most recent past values plus a white-noise error. It is the building block of the Box-Jenkins family of time-series models and is widely used for forecasting stationary economic and financial series. |
| ScholarGateZbiór danych ↗ |
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