方法对比
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| 非线性自回归分布式滞后（NARDL）模型× | 光滑转换自回归 (STAR) 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 | 1994 |
| 提出者≠ | Shin, Yu & Greenwood-Nimmo | Teräsvirta (1994); van Dijk, Teräsvirta & Franses (2002) |
| 类型≠ | Asymmetric cointegration / error-correction model | Nonlinear time-series regime-switching 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: Sickles, R. & Horrace, W. (Eds.), Festschrift in Honor of Peter Schmidt. Springer. DOI ↗ | Teräsvirta, T. (1994). Specification, Estimation, and Evaluation of Smooth Transition Autoregressive Models. Journal of the American Statistical Association, 89(425), 208–218. DOI ↗ |
| 别名≠ | nonlinear ARDL, asymmetric ARDL, Doğrusal Olmayan ARDL (NARDL) | smooth transition autoregressive model, LSTAR, ESTAR, logistic STAR |
| 相关 | 4 | 4 |
| 摘要≠ | The NARDL model, introduced by Shin, Yu and Greenwood-Nimmo in 2014, extends the ARDL framework to capture asymmetric long-run and short-run relationships, testing whether positive and negative changes in a regressor affect the dependent variable differently. | The Smooth Transition Autoregressive (STAR) model is a nonlinear time-series model, developed in Teräsvirta's 1994 framework, that lets the dynamics move smoothly rather than abruptly between two regimes. The logistic variant (LSTAR) captures asymmetric business cycles and the exponential variant (ESTAR) captures purchasing-power-parity deviations. |
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