Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Modelis ar nelineāru slīdošo vidējo (NMV)× | Nelineārais autoregresijas (NAR) modelis× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1978 | 1978-1990 |
| Autors≠ | Granger & Andersen (bilinear/NMA framework); Tong (nonlinear time series theory) | Tong, H. (threshold AR); Terasvirta, T. (STAR variant) |
| Tips | Nonlinear time series model | Nonlinear time series model |
| Pirmavots≠ | Granger, C. W. J., & Andersen, A. P. (1978). An Introduction to Bilinear Time Series Models. Vandenhoeck and Ruprecht, Gottingen. link ↗ | Tong, H. (1990). Non-Linear Time Series: A Dynamical System Approach. Oxford University Press. ISBN: 9780198522201 |
| Citi nosaukumi | NMA model, nonlinear moving average, NLMA model, nonlinear MA | NAR model, nonlinear autoregression, NLAR, threshold autoregressive model |
| Saistītās≠ | 4 | 6 |
| Kopsavilkums≠ | The Nonlinear Moving Average (NMA) model extends the classical linear MA model by allowing the current observation to depend on past innovations through a nonlinear function rather than a simple weighted sum. It is used in time series analysis when error shocks transmit to outcomes in an asymmetric or state-dependent fashion. | 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. |
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