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| Epälineaarinen Zivot-Andrewsin yksikköjuuritesti× | Zivot-Andrews -yksikköjuuritesti yhdellä rakenteellisella muutoksella× | |
|---|---|---|
| Tieteenala | Ekonometria | Ekonometria |
| Menetelmäperhe≠ | Regression model | Hypothesis test |
| Syntyvuosi≠ | 2000s–2010s | 1992 |
| Kehittäjä≠ | Extension combining Zivot & Andrews (1992) with nonlinear STAR-type adjustment; attributed to several applied time-series authors | Eric Zivot & Donald Andrews |
| Tyyppi≠ | Unit root test with structural break and nonlinear adjustment | Sequential unit-root test with endogenous break-point selection |
| Alkuperäislähde | 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 ↗ | 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 ↗ |
| Rinnakkaisnimet | NZA test, nonlinear structural break unit root test, Zivot-Andrews test with nonlinear adjustment, smooth transition Zivot-Andrews test | ZA Test, Zivot-Andrews Break Test, Endogenous Break Unit-Root Test, Zivot-Andrews Birim Kök Testi |
| Liittyvät≠ | 2 | 3 |
| Tiivistelmä≠ | 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 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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