Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Надійний тест Зівота-Ендрюса× | Тест Живота-Ендрюса на одиничний корінь з одним структурним розривом× | |
|---|---|---|
| Галузь | Економетрика | Економетрика |
| Родина≠ | Regression model | Hypothesis test |
| Рік появи≠ | 1992 (original); 2000s (robust variants) | 1992 |
| Автор методу≠ | Zivot & Andrews (1992); robust extensions by subsequent literature | Eric Zivot & Donald Andrews |
| Тип≠ | Unit root test with endogenous structural break | Sequential unit-root test with endogenous break-point selection |
| Основоположне джерело | 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 ↗ |
| Інші назви | robust ZA test, ZA test with robust inference, Zivot-Andrews test with heteroscedasticity-robust critical values, structural break unit root test | ZA Test, Zivot-Andrews Break Test, Endogenous Break Unit-Root Test, Zivot-Andrews Birim Kök Testi |
| Пов'язані≠ | 5 | 3 |
| Підсумок≠ | 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 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. |
| ScholarGateНабір даних ↗ |
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