Compara mètodes

	Model de Lag Distribuït Autorregressiu No Lineal (NARDL)	Test de causalitat de Granger
Camp	Econometria	Econometria
Família	Regression model	Regression model
Any d'origen	2014	1969
Autor original	Shin, Yu, and Greenwood-Nimmo	Clive W. J. Granger
Tipus	Nonlinear cointegration model	Time-series predictive causality test
Font seminal	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.
Àlies	NARDL, nonlinear ARDL, asymmetric ARDL, nonlinear bounds test	Granger causality test, Granger non-causality test, predictive causality test, Granger Nedensellik Testi
Relacionats	4	5
Resum	The Nonlinear ARDL (NARDL) model extends the linear ARDL bounds-testing framework to allow asymmetric long-run and short-run relationships. By decomposing an explanatory variable into its positive and negative partial sums, it tests whether increases and decreases in a regressor have different effects on the dependent variable — a feature that linear cointegration methods cannot capture.	The Granger causality test, introduced by Clive W. J. Granger in 1969, assesses whether the past values of one time series help predict another beyond what the latter's own past already explains. It defines causality in a strictly predictive sense rather than as a structural or physical cause.