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| Bayesian NARDL: Autoregressió Distribuïda No Lineal amb Estimació Bayesiana× | Test de límits bayesià ARDL× | |
|---|---|---|
| Camp | Econometria | Econometria |
| Família | Regression model | Regression model |
| Any d'origen≠ | 2014 (NARDL); Bayesian extension c. 2015–2020 | 2001 (ARDL); Bayesian extension 2010s |
| Autor original≠ | Shin, Yu & Greenwood-Nimmo (NARDL base); Bayesian extension developed in subsequent applied literature | Pesaran, Shin & Smith (ARDL framework, 2001); Bayesian adaptation by subsequent literature |
| Tipus≠ | Nonlinear cointegrating model with Bayesian inference | Cointegration / bounds testing |
| Font seminal≠ | 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. link ↗ | 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. DOI ↗ |
| Àlies | Bayesian NARDL, Bayesian nonlinear ARDL, Bayesian asymmetric ARDL, B-NARDL | Bayesian ARDL, Bayesian bounds testing approach, Bayes ARDL cointegration, Bayesian PSS bounds test |
| Relacionats≠ | 6 | 5 |
| Resum≠ | 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. |
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