Сравнение на методи
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| Нелинеен авторегресивен (NAR) модел× | АРСС модел (авторегресионна плъзгаща се средна)× | |
|---|---|---|
| Област | Иконометрия | Иконометрия |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 1978-1990 | 1970 |
| Създател≠ | Tong, H. (threshold AR); Terasvirta, T. (STAR variant) | George E. P. Box and Gwilym M. Jenkins |
| Тип≠ | Nonlinear time series model | Time series model |
| Основополагащ източник≠ | Tong, H. (1990). Non-Linear Time Series: A Dynamical System Approach. Oxford University Press. ISBN: 9780198522201 | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ |
| Други названия | NAR model, nonlinear autoregression, NLAR, threshold autoregressive model | ARMA, Box-Jenkins model, autoregressive moving average, AR(p)MA(q) |
| Свързани≠ | 6 | 5 |
| Резюме≠ | The Nonlinear AR model extends the classical autoregressive framework by allowing the mapping from past values to the current value to follow an arbitrary or regime-switching nonlinear function. Major families include the Self-Exciting Threshold AR (SETAR), Smooth Transition AR (STAR), and neural network AR, each capturing different forms of asymmetry, regime shifts, or smooth nonlinear dynamics in univariate time series. | 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 ↗ |
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