Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Tijdsvariërende Parameter KPSS Test× | Phillips-Perron (PP) eenheidsworteltest× | |
|---|---|---|
| Vakgebied | Econometrie | Econometrie |
| Familie | Regression model | Regression model |
| Jaar van ontstaan≠ | 2000s-2010s | 1988 |
| Grondlegger≠ | Extension of Kwiatkowski, Phillips, Schmidt, and Shin (1992); time-varying generalizations developed by Cavaliere, Taylor, and others | Peter C. B. Phillips & Pierre Perron |
| Type≠ | Hypothesis test (stationarity) | Unit-root test for stationarity |
| Oorspronkelijke bron≠ | Kwiatkowski, D., Phillips, P. C. B., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root? Journal of Econometrics, 54(1-3), 159-178. DOI ↗ | Phillips, P. C. B., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335–346. DOI ↗ |
| Aliassen≠ | TVP-KPSS test, time-varying KPSS stationarity test, locally stationary KPSS test, TV-KPSS | PP test, Phillips-Perron unit root test, Phillips-Perron birim kök testi |
| Verwant≠ | 3 | 4 |
| Samenvatting≠ | The time-varying parameter KPSS test extends the classic Kwiatkowski-Phillips-Schmidt-Shin (1992) stationarity test to settings where the deterministic or stochastic components of a series may shift over time. It tests the null hypothesis of stationarity while allowing the model's parameters to evolve, making it robust to structural instability that would otherwise distort the standard KPSS result. | The Phillips-Perron test, proposed by Peter Phillips and Pierre Perron in 1988, tests for a unit root in a time series, like the Augmented Dickey-Fuller test, but corrects for autocorrelation and heteroskedasticity in the errors non-parametrically rather than by adding lagged differences. It runs a simple Dickey-Fuller regression and then adjusts the test statistic using a long-run variance estimate, so the practitioner need not choose a lag length for the regression itself. |
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