Comparer des méthodes

	Modèle ARDL non linéaire (NARDL)	Test de causalité de Granger
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	2014	1969
Auteur d'origine	Shin, Yu & Greenwood-Nimmo	Clive W. J. Granger
Type	Nonlinear cointegration model	Causality test (F-test on VAR)
Source fondatrice	Shin, Y., Yu, B., & Greenwood-Nimmo, M. (2014). Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In R. C. Sickles & W. C. Horrace (Eds.), Festschrift in Honor of Peter Schmidt: Econometric Methods and Applications (pp. 281–314). Springer.	Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424–438.
Alias	NARDL, nonlinear bounds test, asymmetric ARDL, asymmetric cointegration model	Granger test, GC test, predictive causality test, Granger non-causality test
Apparentées	5	5
Résumé	The Nonlinear ARDL (NARDL) model extends the linear ARDL bounds-testing framework to allow asymmetric long-run and short-run relationships. By decomposing the regressor into cumulative positive and negative partial sums, it tests whether increases and decreases in a variable exert different effects on the outcome — a feature especially relevant in financial and energy economics where positive and negative shocks rarely cancel out symmetrically.	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.