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| Modello ARIMA Non Lineare× | Modello ARIMA (Autoregressive Integrated Moving Average)× | |
|---|---|---|
| Campo | Econometria | Econometria |
| Famiglia | Regression model | Regression model |
| Anno di origine≠ | 1978-1994 | 1970 |
| Ideatore≠ | Howell Tong (SETAR/TAR framework); Timo Terasvirta (STAR extensions) | 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: 9780198522249 | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ |
| Alias | nonlinear ARIMA, NARIMA, nonlinear time series model, nonlinear Box-Jenkins model | ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q) |
| Correlati≠ | 3 | 6 |
| Sintesi≠ | The Nonlinear ARIMA model extends the classical Box-Jenkins ARIMA framework by allowing the conditional mean of a time series to depend on past values and past errors through a nonlinear function. It encompasses families such as Threshold AR (TAR/SETAR), Smooth Transition AR (STAR/LSTAR/ESTAR), and Markov-switching models, capturing asymmetric dynamics, regime changes, and business-cycle asymmetries that linear ARIMA cannot represent. | 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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