Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Модел на Фурие за AR× | АРСС модел (авторегресионна плъзгаща се средна)× | |
|---|---|---|
| Област | Иконометрия | Иконометрия |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 2012 | 1970 |
| 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. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ |
| Други названия | Fourier AR, trigonometric AR model, smooth transition AR with Fourier terms, FAR model | ARMA, Box-Jenkins model, autoregressive moving average, AR(p)MA(q) |
| Свързани≠ | 6 | 5 |
| 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. | The ARMA(p,q) model describes a stationary time series as a combination of two components: an autoregressive part that regresses the current value on its own past p values, and a moving average part that accounts for past q error terms. It is the foundational framework of the Box-Jenkins methodology for univariate time series modelling and short-run forecasting. |
| ScholarGateНабор от данни ↗ |
|
|