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NARDL Bayesiano: ARDL non lineare con stima Bayesiana×Modello ARDL Non Lineare (NARDL)×
CampoEconometriaEconometria
FamigliaRegression modelRegression model
Anno di origine2014 (NARDL); Bayesian extension c. 2015–20202014
IdeatoreShin, Yu & Greenwood-Nimmo (NARDL base); Bayesian extension developed in subsequent applied literatureShin, Yu & Greenwood-Nimmo
TipoNonlinear cointegrating model with Bayesian inferenceNonlinear cointegration model
Fonte seminaleShin, 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. link ↗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. link ↗
AliasBayesian NARDL, Bayesian nonlinear ARDL, Bayesian asymmetric ARDL, B-NARDLNARDL, nonlinear bounds test, asymmetric ARDL, asymmetric cointegration model
Correlati65
SintesiBayesian 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 Nonlinear ARDL (NARDL) model extends the linear ARDL bounds-testing framework to allow asymmetric long-run and short-run relationships. By decomposing the regressor into cumulative positive and negative partial sums, it tests whether increases and decreases in a variable exert different effects on the outcome — a feature especially relevant in financial and energy economics where positive and negative shocks rarely cancel out symmetrically.
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  3. PUBLISHED

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ScholarGateConfronta i metodi: Bayesian NARDL · Nonlinear ARDL. Consultato il 2026-06-15 da https://scholargate.app/it/compare