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| 非线性自回归分布式滞后 (NARDL) 模型× | 恩格尔-格兰杰协整检验× | |
|---|---|---|
| 领域 | 计量经济学 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2014 | 1987 |
| 提出者≠ | Shin, Yu & Greenwood-Nimmo | Robert F. Engle and Clive W. J. Granger |
| 类型≠ | Nonlinear cointegration model | Cointegration test |
| 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 R. C. Sickles & W. C. Horrace (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 | NARDL, nonlinear bounds test, asymmetric ARDL, asymmetric cointegration model | EG cointegration test, Engle-Granger two-step method, residual-based cointegration test, EG test |
| 相关 | 5 | 5 |
| 摘要≠ | The Nonlinear ARDL (NARDL) model extends the linear ARDL bounds-testing framework to allow asymmetric long-run and short-run relationships. By decomposing the regressor into cumulative positive and negative partial sums, it tests whether increases and decreases in a variable exert different effects on the outcome — a feature especially relevant in financial and energy economics where positive and negative shocks rarely cancel out symmetrically. | The Engle-Granger two-step method tests whether two or more non-stationary I(1) time series share a common stochastic trend — that is, whether a linear combination of them is stationary. If cointegration is confirmed, an error-correction model (ECM) can be estimated to capture both short-run dynamics and long-run equilibrium adjustment. |
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