Salīdzināt metodes

	Granger kauzalitāte ar strukturāliem lūzumiem	Toda-Yamamoto (TY) Granger cēloņsakarības pārbaude
Nozare	Ekonometrija	Ekonometrija
Saime	Regression model	Hypothesis test
Izcelsmes gads	1995-2010	1995
Autors	Granger (1969) causality framework extended by Toda & Yamamoto (1995) and Balcilar et al. (2010)	Hiro Toda & Taku Yamamoto
Tips	Hypothesis test / time-series model	Modified Wald test on augmented VAR
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	break-robust Granger causality, Granger causality under regime change, time-varying Granger causality, structural change Granger test	TY Causality Test, Modified Wald Granger Causality, MWALD Test, Toda-Yamamoto Nedensellik Testi
Saistītās	3	3
Kopsavilkums	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.	The Toda-Yamamoto (TY) causality test, introduced by Toda and Yamamoto (1995), provides a robust procedure for testing Granger non-causality in vector autoregressive (VAR) models when the variables may be integrated or cointegrated of arbitrary order. By intentionally over-fitting the VAR with extra lags equal to the maximum integration order, the method bypasses the need for pre-testing cointegration and preserves the standard asymptotic chi-squared distribution of the Wald statistic.