Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Epälineaarinen autoregressiivinen (NAR) malli× | Epälineaarinen vektorivirheenkorjausmalli (Nonlinear VECM)× | |
|---|---|---|
| Tieteenala | Ekonometria | Ekonometria |
| Menetelmäperhe | Regression model | Regression model |
| Syntyvuosi≠ | 1978-1990 | 1989–1998 |
| Kehittäjä≠ | Tong, H. (threshold AR); Terasvirta, T. (STAR variant) | Granger & Lee (1989); Enders & Granger (1998) |
| Tyyppi≠ | Nonlinear time series model | Nonlinear time-series model |
| Alkuperäislähde≠ | 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 ↗ |
| Rinnakkaisnimet | NAR model, nonlinear autoregression, NLAR, threshold autoregressive model | nonlinear VECM, NVECM, threshold VECM, asymmetric VECM |
| Liittyvät≠ | 6 | 2 |
| Tiivistelmä≠ | 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. |
| ScholarGateAineisto ↗ |
|
|