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| Model Autoregressiu No Lineal (NAR)× | Model ARIMA (Autoregressive Integrated Moving Average)× | Model Autoregressiu (AR)× | |
|---|---|---|---|
| Camp | Econometria | Econometria | Econometria |
| Família | Regression model | Regression model | Regression model |
| Any d'origen≠ | 1978-1990 | 1970 | 1970s (popularised 1976) |
| Autor original≠ | Tong, H. (threshold AR); Terasvirta, T. (STAR variant) | George Box and Gwilym Jenkins | George E. P. Box and Gwilym M. Jenkins |
| Tipus≠ | Nonlinear time series model | Time series forecasting model | Time series model |
| Font seminal≠ | 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 ↗ | Box, G. E. P., & Jenkins, G. M. (1976). Time Series Analysis: Forecasting and Control (revised ed.). Holden-Day. ISBN: 978-0816211043 |
| Àlies | NAR model, nonlinear autoregression, NLAR, threshold autoregressive model | ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q) | AR model, AR(p) model, autoregression, AR process |
| Relacionats | 6 | 6 | 6 |
| Resum≠ | 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. | An autoregressive model of order p — AR(p) — expresses the current value of a time series as a linear function of its own p most recent past values plus a white-noise error. It is the building block of the Box-Jenkins family of time-series models and is widely used for forecasting stationary economic and financial series. |
| ScholarGateConjunt de dades ↗ |
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