Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Model Autoregresiv Neliniar (NAR)× | Modelul ARMA (Autoregresiv Medie Mobilă)× | |
|---|---|---|
| Domeniu | Econometrie | Econometrie |
| Familie | Regression model | Regression model |
| Anul apariției≠ | 1978-1990 | 1970 |
| Autorul original≠ | Tong, H. (threshold AR); Terasvirta, T. (STAR variant) | George E. P. Box and Gwilym M. Jenkins |
| Tip≠ | Nonlinear time series model | Time series model |
| Sursa seminală≠ | Tong, H. (1990). Non-Linear Time Series: A Dynamical System Approach. Oxford University Press. ISBN: 9780198522201 | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ |
| Denumiri alternative | NAR model, nonlinear autoregression, NLAR, threshold autoregressive model | ARMA, Box-Jenkins model, autoregressive moving average, AR(p)MA(q) |
| Înrudite≠ | 6 | 5 |
| Rezumat≠ | 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 ARMA(p,q) model describes a stationary time series as a combination of two components: an autoregressive part that regresses the current value on its own past p values, and a moving average part that accounts for past q error terms. It is the foundational framework of the Box-Jenkins methodology for univariate time series modelling and short-run forecasting. |
| ScholarGateSet de date ↗ |
|
|