Confronta i metodi
Esamina i metodi selezionati fianco a fianco; le righe che differiscono sono evidenziate.
| Modello ARMA (Autoregressive Moving Average)× | Modello ARDL Non Lineare (NARDL)× | |
|---|---|---|
| Campo | Econometria | Econometria |
| Famiglia | Regression model | Regression model |
| Anno di origine≠ | 1970 | 2014 |
| Ideatore≠ | George E. P. Box and Gwilym M. Jenkins | Shin, Yu & Greenwood-Nimmo |
| Tipo≠ | Time series model | Nonlinear cointegration model |
| Fonte seminale≠ | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ | Shin, Y., Yu, B., & Greenwood-Nimmo, M. (2014). Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In R. C. Sickles & W. C. Horrace (Eds.), Festschrift in Honor of Peter Schmidt: Econometric Methods and Applications (pp. 281–314). Springer. link ↗ |
| Alias | ARMA, Box-Jenkins model, autoregressive moving average, AR(p)MA(q) | NARDL, nonlinear bounds test, asymmetric ARDL, asymmetric cointegration model |
| Correlati | 5 | 5 |
| Sintesi≠ | 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. | The Nonlinear ARDL (NARDL) model extends the linear ARDL bounds-testing framework to allow asymmetric long-run and short-run relationships. By decomposing the regressor into cumulative positive and negative partial sums, it tests whether increases and decreases in a variable exert different effects on the outcome — a feature especially relevant in financial and energy economics where positive and negative shocks rarely cancel out symmetrically. |
| ScholarGateInsieme di dati ↗ |
|
|