ScholarGate
Assistant

Comparer des méthodes

Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.

Bayesian NARDL : estimation bayésienne du modèle ARDL non linéaire×Modèle ARDL non linéaire (NARDL)×
DomaineÉconométrieÉconométrie
FamilleRegression modelRegression model
Année d'origine2014 (NARDL); Bayesian extension c. 2015–20202014
Auteur d'origineShin, Yu & Greenwood-Nimmo (NARDL base); Bayesian extension developed in subsequent applied literatureShin, Yu & Greenwood-Nimmo
TypeNonlinear cointegrating model with Bayesian inferenceNonlinear cointegration model
Source fondatriceShin, 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
Apparentées65
RésuméBayesian 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.
ScholarGateJeu de données
  1. v1
  2. 2 Sources
  3. PUBLISHED
  1. v1
  2. 2 Sources
  3. PUBLISHED

Aller à la recherche Télécharger les diapositives

ScholarGateComparer des méthodes: Bayesian NARDL · Nonlinear ARDL. Consulté le 2026-06-17 sur https://scholargate.app/fr/compare