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| ARIMA-Modell (Autoregressives integriertes gleitendes Durchschnittsmodell)× | Vektorautoregression (VAR)× | |
|---|---|---|
| Fachgebiet | Ökonometrie | Ökonometrie |
| Familie | Regression model | Regression model |
| Entstehungsjahr≠ | 1970 | 1980 |
| Urheber≠ | George Box and Gwilym Jenkins | Christopher A. Sims |
| Typ≠ | Time series forecasting model | Multivariate time-series model |
| Wegweisende Quelle≠ | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ | Sims, C. A. (1980). Macroeconomics and Reality. Econometrica, 48(1), 1–48. DOI ↗ |
| Aliasnamen | ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q) | VAR, VAR model, vector autoregressive model, multivariate autoregression |
| Verwandt≠ | 6 | 5 |
| Zusammenfassung≠ | 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. | Vector Autoregression is a multivariate time-series model in which each variable is regressed on its own lags and the lags of all other variables in the system. Originally proposed by Sims (1980) as a data-driven alternative to large structural macroeconomic models, VAR has become the standard workhorse for dynamic analysis in empirical economics and finance. |
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