Comparer des méthodes

	Test de Causalité de Granger Non Linéaire	Test de causalité de Granger
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1992-2006	1969
Auteur d'origine	Baek & Brock (1992); Hiemstra & Jones (1994); Diks & Panchenko (2006)	Clive W. J. Granger
Type	Nonparametric causality test	Causality test (F-test on VAR)
Source fondatrice	Diks, C., & Panchenko, V. (2006). A new statistic and practical guidelines for nonparametric Granger causality testing. Journal of Economic Dynamics and Control, 30(9-10), 1647-1669.	Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424–438.
Alias	nonlinear causality test, BDS-based causality, Diks-Panchenko test, nonparametric Granger causality	Granger test, GC test, predictive causality test, Granger non-causality test
Apparentées	6	5
Résumé	Nonlinear Granger causality extends the classic linear Granger causality framework to detect predictive relationships that operate through nonlinear dynamics. Using nonparametric or semi-parametric statistics based on correlation integrals or kernel density estimation, it identifies whether past values of one variable improve forecasts of another beyond what any linear model can capture.	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.