Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Нелинеен авторегресивен (NAR) модел× | Нелинеен векторeн модел за корекция на грешки (Nonlinear VECM)× | |
|---|---|---|
| Област | Иконометрия | Иконометрия |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 1978-1990 | 1989–1998 |
| Създател≠ | Tong, H. (threshold AR); Terasvirta, T. (STAR variant) | Granger & Lee (1989); Enders & Granger (1998) |
| Тип≠ | Nonlinear time series model | Nonlinear time-series model |
| Основополагащ източник≠ | Tong, H. (1990). Non-Linear Time Series: A Dynamical System Approach. Oxford University Press. ISBN: 9780198522201 | Enders, W., & Granger, C. W. J. (1998). Unit-root tests and asymmetric adjustment with an example using the term structure of interest rates. Journal of Business & Economic Statistics, 16(3), 304–311. DOI ↗ |
| Други названия | NAR model, nonlinear autoregression, NLAR, threshold autoregressive model | nonlinear VECM, NVECM, threshold VECM, asymmetric VECM |
| Свързани≠ | 6 | 2 |
| 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 Nonlinear VECM extends the standard linear VECM by allowing the speed of adjustment toward long-run equilibrium to differ depending on the sign, magnitude, or regime of deviations from that equilibrium. It captures asymmetric or threshold-driven dynamics in cointegrated time-series systems that a standard VECM would miss. |
| ScholarGateНабор от данни ↗ |
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