Confronta i metodi

	NARDL Bayesiano: ARDL non lineare con stima Bayesiana	Modello Bayesiano a Correzione d'Errore Vettoriale (Bayesian VECM)
Campo	Econometria	Econometria
Famiglia	Regression model	Regression model
Anno di origine	2014 (NARDL); Bayesian extension c. 2015–2020	2002–2005
Ideatore	Shin, Yu & Greenwood-Nimmo (NARDL base); Bayesian extension developed in subsequent applied literature	Kleibergen & Paap; Villani
Tipo	Nonlinear cointegrating model with Bayesian inference	Bayesian multivariate time series model
Fonte seminale	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.	Kleibergen, F., & Paap, R. (2002). Priors, posteriors and Bayes factors for a Bayesian analysis of cointegration. Journal of Econometrics, 111(2), 223–249.
Alias	Bayesian NARDL, Bayesian nonlinear ARDL, Bayesian asymmetric ARDL, B-NARDL	Bayesian VECM, B-VECM, Bayesian cointegrated VAR, Bayesian vector error correction
Correlati	6	5
Sintesi	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 VECM combines the classical Vector Error Correction Model — which captures both short-run dynamics and long-run cointegrating relationships among non-stationary multivariate time series — with Bayesian prior distributions over the cointegrating rank and coefficient matrices. This allows principled uncertainty quantification, incorporation of economic theory as priors, and coherent inference even in small samples.