Võrdle meetodeid
Vaata valitud meetodeid kõrvuti; erinevad read on esile tõstetud.
| Fourier Granger'i põhjuslikkuse test× | Toda-Yamamoto põhjuslikkuse test× | |
|---|---|---|
| Valdkond | Ökonomeetria | Ökonomeetria |
| Perekond | Regression model | Regression model |
| Tekkeaasta≠ | 2016 | 1995 |
| Looja≠ | Enders and Jones | Toda, H. Y. and Yamamoto, T. |
| Tüüp | Causality test | Causality test |
| Algallikas≠ | Enders, W., & Jones, P. (2016). Grain prices, oil prices, and multiple smooth breaks in a VAR. Studies in Nonlinear Dynamics and Econometrics, 20(4), 399–419. 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 ↗ |
| Rööpnimetused | Fourier Granger causality test, Enders-Jones Granger causality, smooth structural break Granger test, spectral Granger causality | Toda-Yamamoto test, TY causality test, modified Wald test for Granger causality, TY-MWALD |
| Seotud≠ | 6 | 5 |
| Kokkuvõte≠ | The Fourier Granger causality test extends the classic Granger causality framework by embedding low-frequency Fourier terms in the VAR equation, allowing the causal relationship to shift gradually over time without requiring the researcher to pre-specify the number or location of structural breaks. | 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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