Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Test de cointégration d'Engle-Granger× | Test de racine unitaire de Phillips-Perron× | |
|---|---|---|
| Domaine | Économétrie | Économétrie |
| Famille | Regression model | Regression model |
| Année d'origine≠ | 1987 | 1988 |
| Auteur d'origine≠ | Robert F. Engle and Clive W. J. Granger | Peter C. B. Phillips and Pierre Perron |
| Type≠ | Cointegration test | Hypothesis test (unit root) |
| Source fondatrice≠ | Engle, R. F., & Granger, C. W. J. (1987). Co-integration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251–276. DOI ↗ | Phillips, P. C. B., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335–346. DOI ↗ |
| Alias | EG cointegration test, Engle-Granger two-step method, residual-based cointegration test, EG test | PP test, PP unit root test, Phillips-Perron test, nonparametric unit root test |
| Apparentées | 5 | 5 |
| Résumé≠ | 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. | The Phillips-Perron (PP) test is a nonparametric unit root test for time series that corrects for serial correlation and heteroscedasticity in the error term without adding lagged differences. Introduced by Phillips and Perron (1988), it applies a kernel-based long-run variance estimator to adjust the Dickey-Fuller statistic, making it robust to a wide class of weakly dependent error processes. |
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