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| 结构性断裂格兰杰因果关系× | 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≠ | 1995-2010 | 1995 |
| 提出者≠ | Granger (1969) causality framework extended by Toda & Yamamoto (1995) and Balcilar et al. (2010) | Hiro Toda & Taku Yamamoto |
| 类型≠ | Hypothesis test / time-series model | Modified Wald test on augmented VAR |
| 开创性文献≠ | Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1-2), 225-250. 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 | break-robust Granger causality, Granger causality under regime change, time-varying Granger causality, structural change Granger test | TY Causality Test, Modified Wald Granger Causality, MWALD Test, Toda-Yamamoto Nedensellik Testi |
| 相关 | 3 | 3 |
| 摘要≠ | Structural break Granger causality extends the classic Granger causality framework to accommodate regime shifts and parameter instability in time series. By detecting break points and testing causality within sub-samples or via rolling/recursive windows, it reveals whether a predictive relationship between variables switches on, switches off, or changes direction over time. | 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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