Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Модель AR с Фурье-членами× | Модель ARIMA (авторегрессионная интегрированная скользящая средняя)× | |
|---|---|---|
| Область | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 2012 | 1970 |
| Автор метода≠ | Enders & Lee | George Box and Gwilym Jenkins |
| Тип≠ | Time series model with Fourier augmentation | Time series forecasting model |
| Основополагающий источник≠ | 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. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ |
| Другие названия | Fourier AR, trigonometric AR model, smooth transition AR with Fourier terms, FAR model | ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q) |
| Связанные | 6 | 6 |
| Сводка≠ | 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. | The ARIMA(p,d,q) model is the standard workhorse for univariate time series forecasting. It combines autoregressive terms (past values), differencing to induce stationarity, and moving average terms (past shocks) into a unified linear framework. Developed by Box and Jenkins (1970), it remains one of the most widely applied models in econometrics and applied statistics. |
| ScholarGateНабор данных ↗ |
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