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Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Test de racine unitaire LM de Lee-Strazicich avec deux ruptures structurelles× | Test de racine unitaire augmenté de Dickey-Fuller (ADF)× | Test de racine unitaire de Lumsdaine-Papell avec deux ruptures structurelles× | Test de racine unitaire de Zivot-Andrews avec une rupture structurelle× | |
|---|---|---|---|---|
| Domaine | Économétrie | Économétrie | Économétrie | Économétrie |
| Famille≠ | Hypothesis test | Regression model | Hypothesis test | Hypothesis test |
| Année d'origine≠ | 2003 | 1979 | 1997 | 1992 |
| Auteur d'origine≠ | Junsoo Lee & Mark Strazicich | David A. Dickey & Wayne A. Fuller | Robin Lumsdaine & David Papell | Eric Zivot & Donald Andrews |
| Type≠ | Lagrange Multiplier unit-root test with two endogenous structural breaks | Unit-root test for stationarity | Sequential two-break unit-root test | Sequential unit-root test with endogenous break-point selection |
| Source fondatrice≠ | Lee, J., & Strazicich, M. C. (2003). Minimum Lagrange multiplier unit root test with two structural breaks. Review of Economics and Statistics, 85(4), 1082–1089. DOI ↗ | Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366a), 427–431. DOI ↗ | Lumsdaine, R. L., & Papell, D. H. (1997). Multiple trend breaks and the unit-root hypothesis. Review of Economics and Statistics, 79(2), 212–218. DOI ↗ | 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 ↗ |
| Alias | LS Unit Root Test, Minimum LM Unit Root Test, Lee-Strazicich Two-Break Test, Lee-Strazicich LM Testi | ADF test, Dickey-Fuller test, unit root test, Genişletilmiş Dickey-Fuller testi | LP Test, Two-Break Unit-Root Test, Double Structural Break Unit-Root Test, Lumsdaine-Papell İki Kırılmalı Birim Kök Testi | ZA Test, Zivot-Andrews Break Test, Endogenous Break Unit-Root Test, Zivot-Andrews Birim Kök Testi |
| Apparentées≠ | 3 | 4 | 3 | 3 |
| Résumé≠ | The Lee-Strazicich (2003) test is a Lagrange Multiplier-based unit-root test that allows for two endogenous structural breaks under both the null and alternative hypotheses. Proposed by Junsoo Lee and Mark C. Strazicich, it corrects a fundamental flaw in earlier break-based tests such as Zivot-Andrews, where structural breaks were permitted only under the alternative. By incorporating breaks under the null, the LS test avoids spurious rejections and provides size-correct inference in the presence of level or trend shifts. | The Augmented Dickey-Fuller (ADF) test is the most widely used test for a unit root — that is, for whether a time series is non-stationary and must be differenced before modelling. Introduced by David Dickey and Wayne Fuller in 1979 and extended by Said and Dickey in 1984 to series with higher-order autocorrelation, it regresses the change in the series on its lagged level plus lagged differences and asks whether the lagged-level coefficient is zero. | The Lumsdaine-Papell test, introduced by Robin Lumsdaine and David Papell in 1997, extends the Zivot-Andrews single-break unit-root test to allow for two simultaneous structural breaks in the intercept and/or linear trend of a time series. It is widely used in macroeconomics and finance when data are suspected to have experienced two major regime shifts — such as policy changes, financial crises, or wars — and the researcher needs to determine whether the series is nonetheless integrated of order one. | The Zivot-Andrews (ZA) test, introduced by Eric Zivot and Donald Andrews in 1992, is a sequential unit-root test that allows for a single structural break at an unknown date. It extends the augmented Dickey-Fuller framework by endogenously selecting the break point that provides the strongest evidence against the unit-root null hypothesis, making it particularly useful for macroeconomic and financial time series that may have been disrupted by events such as policy changes, financial crises, or supply shocks. |
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