Comparer des méthodes

	Bayesian NARDL : estimation bayésienne du modèle ARDL non linéaire	Test des bornes bayésien ARDL
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	2014 (NARDL); Bayesian extension c. 2015–2020	2001 (ARDL); Bayesian extension 2010s
Auteur d'origine	Shin, Yu & Greenwood-Nimmo (NARDL base); Bayesian extension developed in subsequent applied literature	Pesaran, Shin & Smith (ARDL framework, 2001); Bayesian adaptation by subsequent literature
Type	Nonlinear cointegrating model with Bayesian inference	Cointegration / bounds testing
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.	Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 289-326.
Alias	Bayesian NARDL, Bayesian nonlinear ARDL, Bayesian asymmetric ARDL, B-NARDL	Bayesian ARDL, Bayesian bounds testing approach, Bayes ARDL cointegration, Bayesian PSS bounds test
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 Bayesian ARDL Bounds Test extends the classical Pesaran-Shin-Smith (2001) bounds testing approach to cointegration by embedding it within a Bayesian inferential framework. Instead of relying on frequentist F- and t-statistics with tabulated critical values, the researcher specifies prior distributions on the model parameters and derives posterior evidence of a long-run level relationship between variables that may be integrated of order zero or one.