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Structurele Breuk Toda-Yamamoto Causaliteitstest×Vector Autoregressie (VAR)×
VakgebiedEconometrieEconometrie
FamilieRegression modelRegression model
Jaar van ontstaan1995 (base); structural break extensions widely adopted 2000s–2010s1980
GrondleggerToda & Yamamoto (1995); structural break extensions by Zivot & Andrews (1992) and subsequent applied literatureChristopher A. Sims
TypeCausality testMultivariate time-series model
Oorspronkelijke bronToda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1-2), 225-250. DOI ↗Sims, C. A. (1980). Macroeconomics and Reality. Econometrica, 48(1), 1–48. DOI ↗
AliassenSB-TY causality, structural break modified Wald test causality, Fourier Toda-Yamamoto causality, causality with regime shiftsVAR, VAR model, vector autoregressive model, multivariate autoregression
Verwant65
SamenvattingThe 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.
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ScholarGateMethoden vergelijken: Structural Break Toda-Yamamoto Causality · Vector Autoregression. Geraadpleegd op 2026-06-18 via https://scholargate.app/nl/compare