方法对比
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| ARIMA（自回归积分滑动平均）模型× | 格兰杰因果检验× | |
|---|---|---|
| 领域 | 计量经济学 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2015 | 1969 |
| 提出者≠ | Box & Jenkins (Box-Jenkins methodology) | Clive W. J. Granger |
| 类型≠ | Univariate time-series model | Time-series predictive causality test |
| 开创性文献≠ | Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021 | Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424-438. DOI ↗ |
| 别名≠ | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | Granger causality test, Granger non-causality test, predictive causality test, Granger Nedensellik Testi |
| 相关 | 5 | 5 |
| 摘要≠ | ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015). | The Granger causality test, introduced by Clive W. J. Granger in 1969, assesses whether the past values of one time series help predict another beyond what the latter's own past already explains. It defines causality in a strictly predictive sense rather than as a structural or physical cause. |
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