Compara mètodes

	Test de causalitat Toda-Yamamoto amb trencament estructural	Granger Causality Test
Camp	Econometria	Econometria
Família	Regression model	Regression model
Any d'origen	1995 (base); structural break extensions widely adopted 2000s–2010s	1969
Autor original	Toda & Yamamoto (1995); structural break extensions by Zivot & Andrews (1992) and subsequent applied literature	Clive W. J. Granger
Tipus	Causality test	Causality test (F-test on VAR)
Font seminal	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.
Àlies	SB-TY causality, structural break modified Wald test causality, Fourier Toda-Yamamoto causality, causality with regime shifts	Granger test, GC test, predictive causality test, Granger non-causality test
Relacionats	6	5
Resum	The structural break Toda-Yamamoto causality test extends the standard Toda-Yamamoto modified Wald (MWALD) procedure to accommodate one or more structural breaks in the time series. By identifying break dates first and then including dummy variables in the augmented VAR, the test maintains its valid asymptotic chi-squared distribution regardless of the integration or cointegration order of the variables, even in the presence of regime shifts.	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.