Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Модель ARIMA (Авторегресійна інтегрована ковзна середня)× | Тест причинності Грейнджера× | |
|---|---|---|
| Галузь | Економетрика | Економетрика |
| Родина | Regression model | Regression model |
| Рік появи≠ | 1970 | 1969 |
| Автор методу≠ | George Box and Gwilym Jenkins | Clive W. J. Granger |
| Тип≠ | Time series forecasting model | Causality test (F-test on VAR) |
| Основоположне джерело≠ | 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 ↗ |
| Інші назви | ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q) | Granger test, GC test, predictive causality test, Granger non-causality test |
| Пов'язані≠ | 6 | 5 |
| Підсумок≠ | 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. |
| ScholarGateНабір даних ↗ |
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