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| Bayesian NARDL× | Estymator GMM Arellano-Bonda× | |
|---|---|---|
| Dziedzina | Ekonometria | Ekonometria |
| Rodzina | Regression model | Regression model |
| Rok powstania≠ | 2014 (NARDL); Bayesian extension c. 2015–2020 | 1991 |
| Twórca≠ | Shin, Yu & Greenwood-Nimmo (NARDL base); Bayesian extension developed in subsequent applied literature | Manuel Arellano and Stephen Bond |
| Typ≠ | Nonlinear cointegrating model with Bayesian inference | GMM estimator for dynamic panel data |
| Źródło pierwotne≠ | 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 ↗ | Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies, 58(2), 277-297. DOI ↗ |
| Inne nazwy | Bayesian NARDL, Bayesian nonlinear ARDL, Bayesian asymmetric ARDL, B-NARDL | AB-GMM, Difference GMM, first-difference GMM, Arellano-Bond estimator |
| Pokrewne≠ | 6 | 5 |
| Podsumowanie≠ | 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 Arellano-Bond GMM estimator is the standard approach for dynamic panel data models in which the lagged dependent variable appears as a regressor. By first-differencing to remove fixed effects and using deeper lags as instruments, it yields consistent estimates even when the error is serially correlated and regressors are endogenous. |
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