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| Modello ARIMA Non Lineare× | Modello di Autoregressione Vettoriale (VAR)× | |
|---|---|---|
| Campo | Econometria | Econometria |
| Famiglia | Regression model | Regression model |
| Anno di origine≠ | 1978-1994 | 2005 |
| Ideatore≠ | Howell Tong (SETAR/TAR framework); Timo Terasvirta (STAR extensions) | Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition |
| Tipo≠ | Nonlinear time series model | Multivariate time-series model |
| Fonte seminale≠ | Tong, H. (1990). Non-Linear Time Series: A Dynamical System Approach. Oxford University Press. ISBN: 9780198522249 | Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer. DOI ↗ |
| Alias | nonlinear ARIMA, NARIMA, nonlinear time series model, nonlinear Box-Jenkins model | vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon |
| Correlati≠ | 3 | 4 |
| 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. | Vector Autoregression is a multivariate time-series model that treats several interdependent series symmetrically, letting each variable depend on its own past values and the past values of all the others. It is the standard tool for capturing mutual causality and joint dynamics, developed in the modern multiple-time-series tradition treated by Lütkepohl (2005). |
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