Comparer des méthodes

	Test de Causalité Bayésien de Toda-Yamamoto	Test de Causalité de Granger de Toda-Yamamoto
Domaine	Économétrie	Économétrie
Famille	Regression model	Hypothesis test
Année d'origine	1995 (base); Bayesian variant developed post-2000	1995
Auteur d'origine	Toda & Yamamoto (1995) for the frequentist base; Bayesian extension by subsequent applied econometricians	Hiro Toda & Taku Yamamoto
Type	Causality test / VAR-based inference	Modified Wald test on augmented VAR
Source fondatrice	Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1-2), 225-250.	Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1–2), 225–250.
Alias	Bayesian TY causality, Bayesian modified Wald causality, Bayesian Granger non-causality in VAR, BTY causality	TY Causality Test, Modified Wald Granger Causality, MWALD Test, Toda-Yamamoto Nedensellik Testi
Apparentées	3	3
Résumé	The Bayesian Toda-Yamamoto causality procedure combines the Toda-Yamamoto VAR augmentation strategy — which sidesteps the need for pre-testing integration and cointegration — with Bayesian prior-posterior updating. It tests Granger non-causality between time series that may be integrated or cointegrated without requiring differencing or error-correction modeling, while incorporating prior information and producing full posterior distributions over the causal parameters.	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.