Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Panel Zivot-Andrews Test voor Structurele Breuken in Eenheidswortels× | Augmented Dickey-Fuller (ADF) eenheidsworteltest× | |
|---|---|---|
| Vakgebied | Econometrie | Econometrie |
| Familie | Regression model | Regression model |
| Jaar van ontstaan≠ | 1992 (panel extension: 2000s) | 1979–1984 |
| Grondlegger≠ | Zivot & Andrews (1992); extended to panel settings by subsequent literature | Said & Dickey (1984); building on Dickey & Fuller (1979) |
| Type≠ | Unit root test with endogenous structural break | Hypothesis test (unit root) |
| Oorspronkelijke bron≠ | 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 ↗ | Said, S. E., & Dickey, D. A. (1984). Testing for unit roots in autoregressive-moving average models of unknown order. Biometrika, 71(3), 599–607. DOI ↗ |
| Aliassen | panel ZA test, panel structural break unit root test, Zivot-Andrews panel unit root test, panel endogenous break unit root test | ADF test, ADF unit root test, Dickey-Fuller test (augmented), Said-Dickey test |
| Verwant≠ | 6 | 5 |
| Samenvatting≠ | The Panel Zivot-Andrews test extends the single-series Zivot-Andrews (1992) structural break unit root test to panel data, allowing each cross-sectional unit to have its own endogenously determined break date. It tests the null of a unit root against the alternative of stationarity with a one-time structural break, accounting for regime shifts that bias standard panel unit root tests toward false non-rejection. | The Augmented Dickey-Fuller test is the standard procedure for determining whether a univariate time series contains a unit root — that is, whether the series is non-stationary. It extends the original Dickey-Fuller test by including lagged difference terms that absorb serial correlation in the residuals, making the test valid for a wide range of time-series processes encountered in economics and finance. |
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