Salīdzināt metodes

	Strukturālās lūzuma Toda-Yamamoto kauzalitātes tests	Granger kauzalitāte ar strukturāliem lūzumiem
Nozare	Ekonometrija	Ekonometrija
Saime	Regression model	Regression model
Izcelsmes gads	1995 (base); structural break extensions widely adopted 2000s–2010s	1995-2010
Autors	Toda & Yamamoto (1995); structural break extensions by Zivot & Andrews (1992) and subsequent applied literature	Granger (1969) causality framework extended by Toda & Yamamoto (1995) and Balcilar et al. (2010)
Tips	Causality test	Hypothesis test / time-series model
Pirmavots	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.
Citi nosaukumi	SB-TY causality, structural break modified Wald test causality, Fourier Toda-Yamamoto causality, causality with regime shifts	break-robust Granger causality, Granger causality under regime change, time-varying Granger causality, structural change Granger test
Saistītās	6	3
Kopsavilkums	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.	Structural break Granger causality extends the classic Granger causality framework to accommodate regime shifts and parameter instability in time series. By detecting break points and testing causality within sub-samples or via rolling/recursive windows, it reveals whether a predictive relationship between variables switches on, switches off, or changes direction over time.