Regression modelEconometrics / time series

Structural Break Toda-Yamamoto Causality Test

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.

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Sources

  1. Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1-2), 225-250. DOI: 10.1016/0304-4076(94)01616-8
  2. Zivot, E., & Andrews, D. W. K. (1992). Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis. Journal of Business and Economic Statistics, 10(3), 251-270. DOI: 10.1080/07350015.1992.10509904

Related methods

ScholarGateStructural Break Toda-Yamamoto Causality (Toda-Yamamoto Causality Test with Structural Breaks). Retrieved 2026-06-04 from https://scholargate.app/en/econometrics/structural-break-toda-yamamoto-causality