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| 稳健Engle-Granger协整检验× | 鲁棒向量纠错模型 (Robust VECM)× | |
|---|---|---|
| 领域 | 计量经济学 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1987 (base); robust variants 2000s–2020s | 1997–2001 |
| 提出者≠ | Engle & Granger (1987); robust extensions by subsequent authors including Hao & Shaffer and others | Sakata & White (1998); Lucas (1997) — robust cointegrated system estimation |
| 类型≠ | Cointegration test | Robust multivariate time-series model |
| 开创性文献≠ | Engle, R. F., & Granger, C. W. J. (1987). Co-integration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251–276. DOI ↗ | Caner, M., & Kilian, L. (2001). Size distortions of tests of the null hypothesis of stationarity: Evidence and implications for the PPP debate. Journal of International Money and Finance, 20(5), 639-657. link ↗ |
| 别名 | robust EG cointegration, outlier-robust cointegration test, robust two-step cointegration, robust EG test | robust VECM, outlier-robust VECM, robust cointegration model, robust VEC model |
| 相关≠ | 5 | 1 |
| 摘要≠ | The Robust Engle-Granger cointegration test adapts the classic two-step Engle-Granger procedure to withstand outliers, heavy-tailed error distributions, and additive noise that can severely distort standard residual-based cointegration inference. By substituting robust regression and robust unit-root testing for classical OLS and ADF steps, it yields reliable conclusions about long-run equilibrium relationships even when the data contain anomalous observations. | Robust VECM extends the classical Vector Error Correction Model by replacing ordinary least squares estimation with outlier-resistant procedures — such as M-estimators, S-estimators, or least trimmed squares — so that cointegration relationships and short-run adjustment dynamics are estimated reliably even when the multivariate time series contains outliers, structural breaks, or heavy-tailed innovations. |
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