Methoden vergleichen

	Toda-Yamamoto-Kausalitätstest	Granger-Kausalitätstest
Fachgebiet	Ökonometrie	Ökonometrie
Familie	Regression model	Regression model
Entstehungsjahr	1995	1969
Urheber	Toda, H. Y. and Yamamoto, T.	Clive W. J. Granger
Typ	Causality test	Causality test (F-test on VAR)
Wegweisende Quelle	Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1-2), 225-250.	Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424–438.
Aliasnamen	Toda-Yamamoto test, TY causality test, modified Wald test for Granger causality, TY-MWALD	Granger test, GC test, predictive causality test, Granger non-causality test
Verwandt	5	5
Zusammenfassung	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.	The Granger causality test is a statistical hypothesis test that determines whether past values of one time series help predict future values of another, beyond what that series' own past already explains. Introduced by Clive Granger in 1969, it is the standard approach for assessing predictive causality in VAR-based time-series analysis.