Comparer des méthodes

	Test de cointégration d'Engle-Granger	Test de causalité de Granger
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1987	1969
Auteur d'origine	Robert F. Engle and Clive W. J. Granger	Clive W. J. Granger
Type	Cointegration test	Causality test (F-test on VAR)
Source fondatrice	Engle, R. F., & Granger, C. W. J. (1987). Co-integration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251–276.	Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424–438.
Alias	EG cointegration test, Engle-Granger two-step method, residual-based cointegration test, EG test	Granger test, GC test, predictive causality test, Granger non-causality test
Apparentées	5	5
Résumé	The Engle-Granger two-step method tests whether two or more non-stationary I(1) time series share a common stochastic trend — that is, whether a linear combination of them is stationary. If cointegration is confirmed, an error-correction model (ECM) can be estimated to capture both short-run dynamics and long-run equilibrium adjustment.	The Granger causality test is a statistical hypothesis test that determines whether past values of one time series help predict future values of another, beyond what that series' own past already explains. Introduced by Clive Granger in 1969, it is the standard approach for assessing predictive causality in VAR-based time-series analysis.