विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| सुदृढ़ जोहानसेन सह-एकीकरण परीक्षण× | Robust Engle-Granger Cointegration Test का सुदृढ़ रूपांतर× | |
|---|---|---|
| क्षेत्र | अर्थमिति | अर्थमिति |
| परिवार | Regression model | Regression model |
| उद्भव वर्ष≠ | 1988–2010 | 1987 (base); robust variants 2000s–2020s |
| प्रवर्तक≠ | Johansen (1988, 1991); robust extensions by Cavaliere, Rahbek, Taylor (2010) and others | Engle & Granger (1987); robust extensions by subsequent authors including Hao & Shaffer and others |
| प्रकार≠ | Cointegration rank test (robust variant) | Cointegration test |
| मौलिक स्रोत≠ | Johansen, S. (1991). Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models. Econometrica, 59(6), 1551–1580. DOI ↗ | Engle, R. F., & Granger, C. W. J. (1987). Co-integration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251–276. DOI ↗ |
| उपनाम | outlier-robust Johansen test, robust trace test, robust maximum eigenvalue test, robust cointegration rank test | robust EG cointegration, outlier-robust cointegration test, robust two-step cointegration, robust EG test |
| संबंधित | 5 | 5 |
| सारांश≠ | The Robust Johansen Cointegration test extends the classical Johansen (1988, 1991) likelihood-ratio framework for determining the cointegrating rank of a multivariate I(1) system to settings where standard Gaussian assumptions fail — in particular when the data exhibit outliers, fat-tailed innovations, or conditional heteroskedasticity. Robust modifications adjust residuals, re-weight observations, or bootstrap critical values so that rank inference remains valid under these violations. | 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. |
| ScholarGateडेटासेट ↗ |
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