Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Kipimo cha Vigezo Vinavyobadilika kwa Wakati cha KPSS× | Jaribio la Usimamishaji la KPSS× | |
|---|---|---|
| Nyanja | Ekonometriki | Ekonometriki |
| Familia | Regression model | Regression model |
| Mwaka wa asili≠ | 2000s-2010s | 1992 |
| Mwanzilishi≠ | Extension of Kwiatkowski, Phillips, Schmidt, and Shin (1992); time-varying generalizations developed by Cavaliere, Taylor, and others | Kwiatkowski, Phillips, Schmidt & Shin |
| Aina≠ | Hypothesis test (stationarity) | Stationarity test (reverse of unit-root tests) |
| Chanzo asilia≠ | 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 ↗ | Kwiatkowski, D., Phillips, P. C. B., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root. Journal of Econometrics, 54(1–3), 159–178. DOI ↗ |
| Majina mbadala≠ | TVP-KPSS test, time-varying KPSS stationarity test, locally stationary KPSS test, TV-KPSS | Kwiatkowski-Phillips-Schmidt-Shin test, stationarity test, KPSS durağanlık testi |
| Zinazohusiana≠ | 3 | 4 |
| Muhtasari≠ | 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 KPSS test, introduced by Kwiatkowski, Phillips, Schmidt and Shin in 1992, tests the null hypothesis that a series is stationary against the alternative that it contains a unit root — the reverse of the ADF and Phillips-Perron tests. By flipping the burden of proof, it is designed to be used alongside unit-root tests so that the two can confirm one another and expose ambiguous, borderline cases. |
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