方法对比
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| 非线性 Granger 因果检验× | 非线性向量误差修正模型（非线性VECM）× | |
|---|---|---|
| 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 | 1989–1998 |
| 提出者≠ | Baek & Brock (1992); Hiemstra & Jones (1994); Diks & Panchenko (2006) | Granger & Lee (1989); Enders & Granger (1998) |
| 类型≠ | Nonparametric causality test | Nonlinear time-series model |
| 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 ↗ | Enders, W., & Granger, C. W. J. (1998). Unit-root tests and asymmetric adjustment with an example using the term structure of interest rates. Journal of Business & Economic Statistics, 16(3), 304–311. DOI ↗ |
| 别名 | nonlinear causality test, BDS-based causality, Diks-Panchenko test, nonparametric Granger causality | nonlinear VECM, NVECM, threshold VECM, asymmetric VECM |
| 相关≠ | 6 | 2 |
| 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 Nonlinear VECM extends the standard linear VECM by allowing the speed of adjustment toward long-run equilibrium to differ depending on the sign, magnitude, or regime of deviations from that equilibrium. It captures asymmetric or threshold-driven dynamics in cointegrated time-series systems that a standard VECM would miss. |
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