Comparer des méthodes

	Test de Causalité de Granger-Fourier	Causalité de Granger avec rupture structurelle
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	2016	1995-2010
Auteur d'origine	Enders and Jones	Granger (1969) causality framework extended by Toda & Yamamoto (1995) and Balcilar et al. (2010)
Type	Causality test	Hypothesis test / time-series model
Source fondatrice	Enders, W., & Jones, P. (2016). Grain prices, oil prices, and multiple smooth breaks in a VAR. Studies in Nonlinear Dynamics and Econometrics, 20(4), 399–419.	Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1-2), 225-250.
Alias	Fourier Granger causality test, Enders-Jones Granger causality, smooth structural break Granger test, spectral Granger causality	break-robust Granger causality, Granger causality under regime change, time-varying Granger causality, structural change Granger test
Apparentées	6	3
Résumé	The Fourier Granger causality test extends the classic Granger causality framework by embedding low-frequency Fourier terms in the VAR equation, allowing the causal relationship to shift gradually over time without requiring the researcher to pre-specify the number or location of structural breaks.	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.