方法对比
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| 恩格尔-格兰杰协整检验× | 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≠ | 1987 | 1969 |
| 提出者≠ | Robert F. Engle and Clive W. J. Granger | Clive W. J. Granger |
| 类型≠ | Cointegration test | Causality test (F-test on VAR) |
| 开创性文献≠ | Engle, R. F., & Granger, C. W. J. (1987). Co-integration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251–276. DOI ↗ | Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424–438. DOI ↗ |
| 别名 | EG cointegration test, Engle-Granger two-step method, residual-based cointegration test, EG test | Granger test, GC test, predictive causality test, Granger non-causality test |
| 相关 | 5 | 5 |
| 摘要≠ | The Engle-Granger two-step method tests whether two or more non-stationary I(1) time series share a common stochastic trend — that is, whether a linear combination of them is stationary. If cointegration is confirmed, an error-correction model (ECM) can be estimated to capture both short-run dynamics and long-run equilibrium adjustment. | 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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