Comparer des méthodes

	Test de causalité de Granger	Test de racine unitaire Augmented Dickey-Fuller (ADF)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1969	1979–1984
Auteur d'origine	Clive W. J. Granger	Said & Dickey (1984); building on Dickey & Fuller (1979)
Type	Causality test (F-test on VAR)	Hypothesis test (unit root)
Source fondatrice	Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424–438.	Said, S. E., & Dickey, D. A. (1984). Testing for unit roots in autoregressive-moving average models of unknown order. Biometrika, 71(3), 599–607.
Alias	Granger test, GC test, predictive causality test, Granger non-causality test	ADF test, ADF unit root test, Dickey-Fuller test (augmented), Said-Dickey test
Apparentées	5	5
Résumé	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.	The Augmented Dickey-Fuller test is the standard procedure for determining whether a univariate time series contains a unit root — that is, whether the series is non-stationary. It extends the original Dickey-Fuller test by including lagged difference terms that absorb serial correlation in the residuals, making the test valid for a wide range of time-series processes encountered in economics and finance.