Salīdzināt metodes

	Toda-Yamamoto (TY) Granger cēloņsakarības pārbaude	Grindžera koeficientu pārbaude
Nozare	Ekonometrija	Ekonometrija
Saime	Hypothesis test	Regression model
Izcelsmes gads	1995	1969
Autors	Hiro Toda & Taku Yamamoto	Clive W. J. Granger
Tips	Modified Wald test on augmented VAR	Time-series predictive causality test
Pirmavots	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.
Citi nosaukumi	TY Causality Test, Modified Wald Granger Causality, MWALD Test, Toda-Yamamoto Nedensellik Testi	Granger causality test, Granger non-causality test, predictive causality test, Granger Nedensellik Testi
Saistītās	3	5
Kopsavilkums	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.	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.