Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Jaribio Imara la Zivot-Andrews× | Lee-Strazicich Test× | |
|---|---|---|
| Nyanja | Ekonometriki | Ekonometriki |
| Familia≠ | Regression model | Hypothesis test |
| Mwaka wa asili≠ | 1992 (original); 2000s (robust variants) | 2003 |
| Mwanzilishi≠ | Zivot & Andrews (1992); robust extensions by subsequent literature | Junsoo Lee & Mark Strazicich |
| Aina≠ | Unit root test with endogenous structural break | Lagrange Multiplier unit-root test with two endogenous structural breaks |
| Chanzo asilia≠ | 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 ↗ |
| Majina mbadala | robust ZA test, ZA test with robust inference, Zivot-Andrews test with heteroscedasticity-robust critical values, structural break unit root test | LS Unit Root Test, Minimum LM Unit Root Test, Lee-Strazicich Two-Break Test, Lee-Strazicich LM Testi |
| Zinazohusiana≠ | 5 | 3 |
| Muhtasari≠ | 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 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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