Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Grangerův test kauzality× | Test Grangerovy kauzality Toda-Yamamoto× | |
|---|---|---|
| Obor | Ekonometrie | Ekonometrie |
| Rodina≠ | Regression model | Hypothesis test |
| Rok vzniku≠ | 1969 | 1995 |
| Tvůrce≠ | Clive W. J. Granger | Hiro Toda & Taku Yamamoto |
| Typ≠ | Time-series predictive causality test | Modified Wald test on augmented VAR |
| Původní zdroj≠ | 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 ↗ |
| Další názvy | 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 |
| Příbuzné≠ | 5 | 3 |
| Shrnutí≠ | 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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