Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Test de rupture structurelle de Zivot-Andrews× | Test de causalité de Granger× | |
|---|---|---|
| Domaine | Économétrie | Économétrie |
| Famille | Regression model | Regression model |
| Année d'origine≠ | 1992 | 1969 |
| Auteur d'origine≠ | Eric Zivot and Donald W. K. Andrews | Clive W. J. Granger |
| Type≠ | Unit root test with endogenous structural break | Causality test (F-test on VAR) |
| Source fondatrice≠ | Zivot, E., & Andrews, D. W. K. (1992). Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis. Journal of Business & Economic Statistics, 10(3), 251–270. DOI ↗ | Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424–438. DOI ↗ |
| Alias | ZA test, Zivot-Andrews unit root test, endogenous structural break unit root test, ZA structural break test | Granger test, GC test, predictive causality test, Granger non-causality test |
| Apparentées≠ | 6 | 5 |
| Résumé≠ | The Zivot-Andrews (ZA) test is a unit root test that endogenously identifies the most likely location of a single structural break in a time series. Unlike the standard ADF test, it does not require the researcher to pre-specify when the break occurred, making it robust to data-driven regime shifts such as policy changes, financial crises, or major economic events. | The Granger causality test is a statistical hypothesis test that determines whether past values of one time series help predict future values of another, beyond what that series' own past already explains. Introduced by Clive Granger in 1969, it is the standard approach for assessing predictive causality in VAR-based time-series analysis. |
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