Módszerek összehasonlítása
Tekintse át a kiválasztott módszereket egymás mellett; az eltérő sorok kiemelve jelennek meg.
| ARIMA (Autoregressive Integrated Moving Average) modell× | Vektor Autoregressziós (VAR) Modell× | |
|---|---|---|
| Tudományterület | Ökonometria | Ökonometria |
| Módszercsalád | Regression model | Regression model |
| Keletkezés éve≠ | 2015 | 2005 |
| Megalkotó≠ | Box & Jenkins (Box-Jenkins methodology) | Lütkepohl (textbook treatment); Sims (1980) macroeconometric tradition |
| Típus≠ | Univariate time-series model | Multivariate time-series model |
| Alapmű≠ | 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 | Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer. DOI ↗ |
| Alternatív nevek≠ | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | vector autoregression, VAR, VAR Modeli (Vektör Otoregresyon), vektör otoregresyon |
| Kapcsolódó≠ | 5 | 4 |
| Összefoglaló≠ | 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). | 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). |
| ScholarGateAdatkészlet ↗ |
|
|