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| Модел на Фурие за AR× | Авторегресивен модел (AR)× | |
|---|---|---|
| Област | Иконометрия | Иконометрия |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 2012 | 1970s (popularised 1976) |
| Създател≠ | Enders & Lee | George E. P. Box and Gwilym M. Jenkins |
| Тип≠ | Time series model with Fourier augmentation | Time series model |
| Основополагащ източник≠ | 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 |
| Други названия | Fourier AR, trigonometric AR model, smooth transition AR with Fourier terms, FAR model | AR model, AR(p) model, autoregression, AR process |
| Свързани | 6 | 6 |
| Резюме≠ | 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. |
| ScholarGateНабор от данни ↗ |
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