Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Nelineārais autoregresijas (NAR) modelis× | Nelineārais ARDL (NARDL) modelis× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1978-1990 | 2014 |
| Autors≠ | Tong, H. (threshold AR); Terasvirta, T. (STAR variant) | Shin, Yu & Greenwood-Nimmo |
| Tips≠ | Nonlinear time series model | Nonlinear cointegration model |
| Pirmavots≠ | Tong, H. (1990). Non-Linear Time Series: A Dynamical System Approach. Oxford University Press. ISBN: 9780198522201 | Shin, Y., Yu, B., & Greenwood-Nimmo, M. (2014). Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In R. C. Sickles & W. C. Horrace (Eds.), Festschrift in Honor of Peter Schmidt: Econometric Methods and Applications (pp. 281–314). Springer. link ↗ |
| Citi nosaukumi | NAR model, nonlinear autoregression, NLAR, threshold autoregressive model | NARDL, nonlinear bounds test, asymmetric ARDL, asymmetric cointegration model |
| Saistītās≠ | 6 | 5 |
| Kopsavilkums≠ | 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 ARDL (NARDL) model extends the linear ARDL bounds-testing framework to allow asymmetric long-run and short-run relationships. By decomposing the regressor into cumulative positive and negative partial sums, it tests whether increases and decreases in a variable exert different effects on the outcome — a feature especially relevant in financial and energy economics where positive and negative shocks rarely cancel out symmetrically. |
| ScholarGateDatu kopa ↗ |
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