Сравнение методов

	Робастный тест на коинтеграцию Энгла-Грейнджера	Робастная модель коррекции ошибок вектора (Robust VECM)
Область	Эконометрика	Эконометрика
Семейство	Regression model	Regression model
Год появления	1987 (base); robust variants 2000s–2020s	1997–2001
Автор метода	Engle & Granger (1987); robust extensions by subsequent authors including Hao & Shaffer and others	Sakata & White (1998); Lucas (1997) — robust cointegrated system estimation
Тип	Cointegration test	Robust multivariate time-series model
Основополагающий источник	Engle, R. F., & Granger, C. W. J. (1987). Co-integration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251–276.	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.
Другие названия	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
Связанные	5	1
Сводка	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.