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| Mchoro wa Kujirejelea Usio na Mstari (NAR)× | Modeli wa Kurekebisha Hitilafu wa Vecta Usio na Mstari (Nonlinear VECM)× | |
|---|---|---|
| Nyanja | Ekonometriki | Ekonometriki |
| Familia | Regression model | Regression model |
| Mwaka wa asili≠ | 1978-1990 | 1989–1998 |
| Mwanzilishi≠ | Tong, H. (threshold AR); Terasvirta, T. (STAR variant) | Granger & Lee (1989); Enders & Granger (1998) |
| Aina≠ | Nonlinear time series model | Nonlinear time-series model |
| Chanzo asilia≠ | 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 ↗ |
| Majina mbadala | NAR model, nonlinear autoregression, NLAR, threshold autoregressive model | nonlinear VECM, NVECM, threshold VECM, asymmetric VECM |
| Zinazohusiana≠ | 6 | 2 |
| Muhtasari≠ | 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. |
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