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| Modello Autoregressivo Non Lineare (NAR)× | Modello ARIMA (Autoregressive Integrated Moving Average)× | |
|---|---|---|
| Campo | Econometria | Econometria |
| Famiglia | Regression model | Regression model |
| Anno di origine≠ | 1978-1990 | 1970 |
| Ideatore≠ | Tong, H. (threshold AR); Terasvirta, T. (STAR variant) | George Box and Gwilym Jenkins |
| Tipo≠ | Nonlinear time series model | Time series forecasting model |
| Fonte seminale≠ | 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 ↗ |
| Alias | NAR model, nonlinear autoregression, NLAR, threshold autoregressive model | ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q) |
| Correlati | 6 | 6 |
| Sintesi≠ | 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 ARIMA(p,d,q) model is the standard workhorse for univariate time series forecasting. It combines autoregressive terms (past values), differencing to induce stationarity, and moving average terms (past shocks) into a unified linear framework. Developed by Box and Jenkins (1970), it remains one of the most widely applied models in econometrics and applied statistics. |
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