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| Модел ARIMA (Авторегресионен интегриран плъзгащ се среден)× | Тест за причинност на Грейнджър× | |
|---|---|---|
| Област | Иконометрия | Иконометрия |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 1970 | 1969 |
| Създател≠ | George Box and Gwilym Jenkins | Clive W. J. Granger |
| Тип≠ | Time series forecasting model | Causality test (F-test on VAR) |
| Основополагащ източник≠ | Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ | Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424–438. DOI ↗ |
| Други названия | ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q) | Granger test, GC test, predictive causality test, Granger non-causality test |
| Свързани≠ | 6 | 5 |
| Резюме≠ | 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 Granger causality test is a statistical hypothesis test that determines whether past values of one time series help predict future values of another, beyond what that series' own past already explains. Introduced by Clive Granger in 1969, it is the standard approach for assessing predictive causality in VAR-based time-series analysis. |
| ScholarGateНабор от данни ↗ |
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