Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Robustní test Zivota a Andrewse× | Bai-Perronův test vícenásobných strukturálních zlomů× | |
|---|---|---|
| Obor | Ekonometrie | Ekonometrie |
| Rodina≠ | Regression model | Hypothesis test |
| Rok vzniku≠ | 1992 (original); 2000s (robust variants) | 1998 |
| Tvůrce≠ | Zivot & Andrews (1992); robust extensions by subsequent literature | Jushan Bai & Pierre Perron |
| Typ≠ | Unit root test with endogenous structural break | Sequential hypothesis test for multiple structural breaks |
| Původní zdroj≠ | 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 ↗ | Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47–78. DOI ↗ |
| Další názvy | robust ZA test, ZA test with robust inference, Zivot-Andrews test with heteroscedasticity-robust critical values, structural break unit root test | Bai-Perron Multiple Break Test, Multiple Structural Change Test, Sequential Structural Break Test, Çoklu Yapısal Kırılma Testi |
| Příbuzné≠ | 5 | 2 |
| Shrnutí≠ | 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 Bai-Perron test, introduced by Jushan Bai and Pierre Perron in their landmark 1998 Econometrica paper, is a least-squares-based procedure for detecting, estimating, and testing the number of structural breaks in a linear regression model estimated on time-series data. Unlike single-break tests, it simultaneously identifies multiple change-points in a sample, providing economists and empirical researchers with a rigorous, data-driven way to locate parameter instability across time. |
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