Comparer des méthodes

	Bayesian NARDL : estimation bayésienne du modèle ARDL non linéaire	Modèle à Correction d'Erreur Vectorielle (VECM)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	2014 (NARDL); Bayesian extension c. 2015–2020	1987
Auteur d'origine	Shin, Yu & Greenwood-Nimmo (NARDL base); Bayesian extension developed in subsequent applied literature	Robert F. Engle and Clive W. J. Granger
Type	Nonlinear cointegrating model with Bayesian inference	Multivariate time-series model
Source fondatrice	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: Econometric Methods and Applications (pp. 281–314). Springer.	Engle, R. F., & Granger, C. W. J. (1987). Co-integration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251–276.
Alias	Bayesian NARDL, Bayesian nonlinear ARDL, Bayesian asymmetric ARDL, B-NARDL	VECM, error correction VAR, cointegrated VAR, vector equilibrium correction model
Apparentées	6	5
Résumé	Bayesian NARDL combines the Nonlinear Autoregressive Distributed Lag framework of Shin, Yu, and Greenwood-Nimmo (2014) with Bayesian posterior inference. It models asymmetric long-run cointegration — allowing positive and negative shocks to a regressor to have different equilibrium effects — while incorporating prior knowledge and producing full posterior distributions over all parameters, including the asymmetry gap.	The Vector Error Correction Model extends the Vector Autoregression (VAR) framework to a system of variables that share one or more long-run equilibrium relationships. It jointly models short-run dynamics and the speed at which each variable corrects back toward equilibrium after a shock, making it the standard tool for analysing cointegrated multivariate time series.