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| Test di radice unitarienne non lineare di Zivot-Andrews× | Test di radice unitaria LM di Lee-Strazicich con due rotture strutturali× | |
|---|---|---|
| Campo | Econometria | Econometria |
| Famiglia≠ | Regression model | Hypothesis test |
| Anno di origine≠ | 2000s–2010s | 2003 |
| Ideatore≠ | Extension combining Zivot & Andrews (1992) with nonlinear STAR-type adjustment; attributed to several applied time-series authors | Junsoo Lee & Mark Strazicich |
| Tipo≠ | Unit root test with structural break and nonlinear adjustment | Lagrange Multiplier unit-root test with two endogenous structural breaks |
| Fonte seminale≠ | 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 ↗ | 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 ↗ |
| Alias | NZA test, nonlinear structural break unit root test, Zivot-Andrews test with nonlinear adjustment, smooth transition Zivot-Andrews test | LS Unit Root Test, Minimum LM Unit Root Test, Lee-Strazicich Two-Break Test, Lee-Strazicich LM Testi |
| Correlati≠ | 2 | 3 |
| Sintesi≠ | The Nonlinear Zivot-Andrews test extends the classical Zivot-Andrews structural-break unit root test by embedding smooth-transition nonlinear adjustment into the test regression. It jointly searches for an endogenous structural break and allows the speed of mean-reversion to vary with distance from the attractor, producing more power against nonlinear stationary alternatives than either test alone. | 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. |
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