方法对比
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| 自回归移动平均模型 (ARMA)× | 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≠ | 1970 | 1969 |
| 提出者≠ | George E. P. Box and Gwilym M. Jenkins | Clive W. J. Granger |
| 类型≠ | Time series model | Causality test (F-test on VAR) |
| 开创性文献≠ | 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 ↗ |
| 别名 | ARMA, Box-Jenkins model, autoregressive moving average, AR(p)MA(q) | Granger test, GC test, predictive causality test, Granger non-causality test |
| 相关 | 5 | 5 |
| 摘要≠ | 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. |
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