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| Autoregressione Vettoriale (VAR)× | Modello ARIMA (Autoregressive Integrated Moving Average)× | |
|---|---|---|
| Campo | Econometria | Econometria |
| Famiglia | Regression model | Regression model |
| Anno di origine≠ | 1980 | 1970 |
| Ideatore≠ | Christopher A. Sims | George Box and Gwilym Jenkins |
| Tipo≠ | Multivariate time-series model | Time series forecasting model |
| Fonte seminale≠ | Sims, C. A. (1980). Macroeconomics and Reality. Econometrica, 48(1), 1–48. DOI ↗ | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ |
| Alias | VAR, VAR model, vector autoregressive model, multivariate autoregression | ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q) |
| Correlati≠ | 5 | 6 |
| Sintesi≠ | 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. | 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. |
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