方法对比
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| 贝叶斯非线性自回归分布滞后模型：具有贝叶斯估计的非线性自回归分布滞后模型× | 面板非线性自回归分布式滞后（Panel NARDL）模型× | |
|---|---|---|
| 领域 | 计量经济学 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2014 (NARDL); Bayesian extension c. 2015–2020 | 2014–2018 |
| 提出者≠ | Shin, Yu & Greenwood-Nimmo (NARDL base); Bayesian extension developed in subsequent applied literature | Shin, Yu & Greenwood-Nimmo (2014), extended to panel settings by subsequent authors |
| 类型≠ | Nonlinear cointegrating model with Bayesian inference | Nonlinear dynamic panel model |
| 开创性文献≠ | 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 ↗ | 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 (pp. 281–314). Springer. DOI ↗ |
| 别名 | Bayesian NARDL, Bayesian nonlinear ARDL, Bayesian asymmetric ARDL, B-NARDL | Panel Nonlinear ARDL, panel asymmetric ARDL, panel NARDL bounds test, nonlinear panel cointegration model |
| 相关≠ | 6 | 4 |
| 摘要≠ | 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. | Panel NARDL extends the time-series NARDL framework of Shin, Yu and Greenwood-Nimmo (2014) to a panel data setting, allowing researchers to detect asymmetric long-run and short-run relationships between variables across multiple cross-sections simultaneously. By decomposing the regressor into positive and negative partial sums, the model tests whether increases and decreases in an explanatory variable have different effects on the outcome. |
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