Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Modelul Vectorial de Autoregresie (VAR)× | Modelul ARIMA (Autoregresiv Integrat cu Medii Mobile)× | |
|---|---|---|
| Domeniu | Econometrie | Econometrie |
| Familie | Regression model | Regression model |
| Anul apariției≠ | 2005 | 2015 |
| Autorul original≠ | Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition | Box & Jenkins (Box-Jenkins methodology) |
| Tip≠ | Multivariate time-series model | Univariate time-series model |
| Sursa seminală≠ | Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer. DOI ↗ | Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021 |
| Denumiri alternative≠ | vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli |
| Înrudite≠ | 4 | 5 |
| Rezumat≠ | 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). | ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015). |
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