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| 格兰杰因果检验× | Toda-Yamamoto Granger 因果检验× | |
|---|---|---|
| 领域 | 计量经济学 | 计量经济学 |
| 方法族≠ | Regression model | Hypothesis test |
| 起源年份≠ | 1969 | 1995 |
| 提出者≠ | Clive W. J. Granger | Hiro Toda & Taku Yamamoto |
| 类型≠ | Time-series predictive causality test | Modified Wald test on augmented VAR |
| 开创性文献≠ | Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424-438. DOI ↗ | Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1–2), 225–250. DOI ↗ |
| 别名 | Granger causality test, Granger non-causality test, predictive causality test, Granger Nedensellik Testi | TY Causality Test, Modified Wald Granger Causality, MWALD Test, Toda-Yamamoto Nedensellik Testi |
| 相关≠ | 5 | 3 |
| 摘要≠ | 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. | The Toda-Yamamoto (TY) causality test, introduced by Toda and Yamamoto (1995), provides a robust procedure for testing Granger non-causality in vector autoregressive (VAR) models when the variables may be integrated or cointegrated of arbitrary order. By intentionally over-fitting the VAR with extra lags equal to the maximum integration order, the method bypasses the need for pre-testing cointegration and preserves the standard asymptotic chi-squared distribution of the Wald statistic. |
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