Comparer des méthodes

	Test de Causalité de Granger Non Linéaire	Test des bornes ARDL non linéaire (NARDL)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1992-2006	2014
Auteur d'origine	Baek & Brock (1992); Hiemstra & Jones (1994); Diks & Panchenko (2006)	Shin, Yu, and Greenwood-Nimmo
Type	Nonparametric causality test	Asymmetric cointegration test
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.	Shin, Y., Yu, B., & Greenwood-Nimmo, M. (2014). Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In W. C. Horrace & R. C. Sickles (Eds.), Festschrift in Honor of Peter Schmidt (pp. 281-314). Springer.
Alias	nonlinear causality test, BDS-based causality, Diks-Panchenko test, nonparametric Granger causality	NARDL, asymmetric ARDL, nonlinear bounds testing approach, NARDL bounds testing
Apparentées	6	1
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 Nonlinear ARDL bounds test, developed by Shin, Yu, and Greenwood-Nimmo (2014), extends the linear ARDL framework to detect asymmetric long-run relationships in time series. By decomposing a regressor into positive and negative partial sums, NARDL simultaneously tests for cointegration and estimates separate long-run effects for increases and decreases — without requiring all variables to be integrated of the same order.