Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Model ARIMA (Autoregresiv Integrat Medie Mobilă)× | Modelul ARDL neliniar (NARDL)× | |
|---|---|---|
| Domeniu | Econometrie | Econometrie |
| Familie | Regression model | Regression model |
| Anul apariției≠ | 1970 | 2014 |
| Autorul original≠ | George Box and Gwilym Jenkins | Shin, Yu & Greenwood-Nimmo |
| Tip≠ | Time series forecasting model | Nonlinear cointegration model |
| Sursa seminală≠ | 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 ↗ |
| Denumiri alternative | ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q) | NARDL, nonlinear bounds test, asymmetric ARDL, asymmetric cointegration model |
| Înrudite≠ | 6 | 5 |
| Rezumat≠ | 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. | 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. |
| ScholarGateSet de date ↗ |
|
|