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| АРСС модел (авторегресионна плъзгаща се средна)× | Тест за причинност на Грейнджър× | |
|---|---|---|
| Област | Иконометрия | Иконометрия |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 1970 | 1969 |
| Създател≠ | George E. P. Box and Gwilym M. Jenkins | Clive W. J. Granger |
| Тип≠ | Time series 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 | ARMA, Box-Jenkins model, autoregressive moving average, AR(p)MA(q) | Granger test, GC test, predictive causality test, Granger non-causality test |
| Свързани | 5 | 5 |
| Резюме≠ | The ARMA(p,q) model describes a stationary time series as a combination of two components: an autoregressive part that regresses the current value on its own past p values, and a moving average part that accounts for past q error terms. It is the foundational framework of the Box-Jenkins methodology for univariate time series modelling and short-run forecasting. | 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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