Methoden vergleichen

	Toda-Yamamoto-Kausalitätstest mit strukturellem Bruch	Vektorautoregression (VAR)
Fachgebiet	Ökonometrie	Ökonometrie
Familie	Regression model	Regression model
Entstehungsjahr	1995 (base); structural break extensions widely adopted 2000s–2010s	1980
Urheber	Toda & Yamamoto (1995); structural break extensions by Zivot & Andrews (1992) and subsequent applied literature	Christopher A. Sims
Typ	Causality test	Multivariate time-series model
Wegweisende Quelle	Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1-2), 225-250.	Sims, C. A. (1980). Macroeconomics and Reality. Econometrica, 48(1), 1–48.
Aliasnamen	SB-TY causality, structural break modified Wald test causality, Fourier Toda-Yamamoto causality, causality with regime shifts	VAR, VAR model, vector autoregressive model, multivariate autoregression
Verwandt	6	5
Zusammenfassung	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.	Vector Autoregression is a multivariate time-series model in which each variable is regressed on its own lags and the lags of all other variables in the system. Originally proposed by Sims (1980) as a data-driven alternative to large structural macroeconomic models, VAR has become the standard workhorse for dynamic analysis in empirical economics and finance.