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× | 向量误差修正模型 (VECM)× | |
|---|---|---|
| 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 | 1987 |
| 提出者≠ | Shin, Yu & Greenwood-Nimmo (NARDL base); Bayesian extension developed in subsequent applied literature | Robert F. Engle and Clive W. J. Granger |
| 类型≠ | Nonlinear cointegrating model with Bayesian inference | Multivariate time-series 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 ↗ | Engle, R. F., & Granger, C. W. J. (1987). Co-integration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251–276. DOI ↗ |
| 别名 | Bayesian NARDL, Bayesian nonlinear ARDL, Bayesian asymmetric ARDL, B-NARDL | VECM, error correction VAR, cointegrated VAR, vector equilibrium correction model |
| 相关≠ | 6 | 5 |
| 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. | The Vector Error Correction Model extends the Vector Autoregression (VAR) framework to a system of variables that share one or more long-run equilibrium relationships. It jointly models short-run dynamics and the speed at which each variable corrects back toward equilibrium after a shock, making it the standard tool for analysing cointegrated multivariate time series. |
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