Salīdzināt metodes

	Granger kauzalitāte ar strukturāliem lūzumiem	Grindžera koeficientu pārbaude
Nozare	Ekonometrija	Ekonometrija
Saime	Regression model	Regression model
Izcelsmes gads	1995-2010	1969
Autors	Granger (1969) causality framework extended by Toda & Yamamoto (1995) and Balcilar et al. (2010)	Clive W. J. Granger
Tips	Hypothesis test / time-series model	Time-series predictive causality test
Pirmavots	Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1-2), 225-250.	Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424-438.
Citi nosaukumi	break-robust Granger causality, Granger causality under regime change, time-varying Granger causality, structural change Granger test	Granger causality test, Granger non-causality test, predictive causality test, Granger Nedensellik Testi
Saistītās	3	5
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 Granger causality test, introduced by Clive W. J. Granger in 1969, assesses whether the past values of one time series help predict another beyond what the latter's own past already explains. It defines causality in a strictly predictive sense rather than as a structural or physical cause.