Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Test de Zivot-Andrews Robuste× | Test de racine unitaire de Phillips-Perron (PP)× | |
|---|---|---|
| Domaine | Économétrie | Économétrie |
| Famille | Regression model | Regression model |
| Année d'origine≠ | 1992 (original); 2000s (robust variants) | 1988 |
| Auteur d'origine≠ | Zivot & Andrews (1992); robust extensions by subsequent literature | Peter C. B. Phillips & Pierre Perron |
| Type≠ | Unit root test with endogenous structural break | Unit-root test for stationarity |
| 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 ↗ | Phillips, P. C. B., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335–346. DOI ↗ |
| Alias≠ | robust ZA test, ZA test with robust inference, Zivot-Andrews test with heteroscedasticity-robust critical values, structural break unit root test | PP test, Phillips-Perron unit root test, Phillips-Perron birim kök testi |
| Apparentées≠ | 5 | 4 |
| Résumé≠ | The Robust Zivot-Andrews test extends the classic Zivot-Andrews (1992) unit root test to provide reliable inference when the error term may be heteroscedastic or non-normal. It tests whether a time series has a unit root while endogenously identifying a single structural break in the level, trend, or both, without requiring the researcher to pre-specify the break date. | The Phillips-Perron test, proposed by Peter Phillips and Pierre Perron in 1988, tests for a unit root in a time series, like the Augmented Dickey-Fuller test, but corrects for autocorrelation and heteroskedasticity in the errors non-parametrically rather than by adding lagged differences. It runs a simple Dickey-Fuller regression and then adjusts the test statistic using a long-run variance estimate, so the practitioner need not choose a lag length for the regression itself. |
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