Salīdzināt metodes

	Beijesa Toda-Yamamoto kauzalitātes tests	Grindžera koeficientu pārbaude
Nozare	Ekonometrija	Ekonometrija
Saime	Regression model	Regression model
Izcelsmes gads	1995 (base); Bayesian variant developed post-2000	1969
Autors	Toda & Yamamoto (1995) for the frequentist base; Bayesian extension by subsequent applied econometricians	Clive W. J. Granger
Tips	Causality test / VAR-based inference	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	Bayesian TY causality, Bayesian modified Wald causality, Bayesian Granger non-causality in VAR, BTY causality	Granger causality test, Granger non-causality test, predictive causality test, Granger Nedensellik Testi
Saistītās	3	5
Kopsavilkums	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 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.