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| Modello ARMA non lineare (NARMA)× | Modello ARMA (Autoregressive Moving Average)× | |
|---|---|---|
| Campo | Econometria | Econometria |
| Famiglia | Regression model | Regression model |
| Anno di origine≠ | 1980s–1990s | 1970 |
| Ideatore≠ | Tong (1990); Granger & Terasvirta (1993) | George E. P. Box and Gwilym M. Jenkins |
| Tipo≠ | Nonlinear time series model | Time series model |
| Fonte seminale≠ | Tong, H. (1990). Non-linear Time Series: A Dynamical System Approach. Oxford University Press. ISBN: 978-0198522300 | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ |
| Alias | NARMA, nonlinear ARMA, NLARMA, nonlinear autoregressive moving average | ARMA, Box-Jenkins model, autoregressive moving average, AR(p)MA(q) |
| Correlati≠ | 2 | 5 |
| Sintesi≠ | The Nonlinear ARMA (NARMA) model extends the classical linear ARMA framework by allowing the conditional mean to depend on past observations and past errors through an arbitrary nonlinear function. It captures complex dynamics — such as regime changes, asymmetric cycles, and threshold effects — that linear models miss, making it valuable for economic and financial 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. |
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