方法对比
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| 非线性 Granger 因果检验× | Toda-Yamamoto 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≠ | 1992-2006 | 1995 |
| 提出者≠ | Baek & Brock (1992); Hiemstra & Jones (1994); Diks & Panchenko (2006) | Toda, H. Y. and Yamamoto, T. |
| 类型≠ | Nonparametric causality test | Causality test |
| 开创性文献≠ | Diks, C., & Panchenko, V. (2006). A new statistic and practical guidelines for nonparametric Granger causality testing. Journal of Economic Dynamics and Control, 30(9-10), 1647-1669. 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 | nonlinear causality test, BDS-based causality, Diks-Panchenko test, nonparametric Granger causality | Toda-Yamamoto test, TY causality test, modified Wald test for Granger causality, TY-MWALD |
| 相关≠ | 6 | 5 |
| 摘要≠ | Nonlinear Granger causality extends the classic linear Granger causality framework to detect predictive relationships that operate through nonlinear dynamics. Using nonparametric or semi-parametric statistics based on correlation integrals or kernel density estimation, it identifies whether past values of one variable improve forecasts of another beyond what any linear model can capture. | The Toda-Yamamoto (TY) causality test is a modified Wald procedure for testing Granger causality in vector autoregressions (VARs) estimated in levels, even when variables are nonstationary or cointegrated. By intentionally over-fitting the VAR with extra lags equal to the maximum integration order, it restores the standard chi-squared asymptotic distribution of the Wald statistic without requiring prior unit-root or cointegration pretesting. |
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