विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| फिलिप्स-पेरॉन यूनिट रूट टेस्ट× | ग्रेंजर कारणता परीक्षण (Granger Causality Test)× | |
|---|---|---|
| क्षेत्र | अर्थमिति | अर्थमिति |
| परिवार | Regression model | Regression model |
| उद्भव वर्ष≠ | 1988 | 1969 |
| प्रवर्तक≠ | Peter C. B. Phillips and Pierre Perron | Clive W. J. Granger |
| प्रकार≠ | Hypothesis test (unit root) | Causality test (F-test on VAR) |
| मौलिक स्रोत≠ | Phillips, P. C. B., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335–346. DOI ↗ | Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37(3), 424–438. DOI ↗ |
| उपनाम | PP test, PP unit root test, Phillips-Perron test, nonparametric unit root test | Granger test, GC test, predictive causality test, Granger non-causality test |
| संबंधित | 5 | 5 |
| सारांश≠ | The Phillips-Perron (PP) test is a nonparametric unit root test for time series that corrects for serial correlation and heteroscedasticity in the error term without adding lagged differences. Introduced by Phillips and Perron (1988), it applies a kernel-based long-run variance estimator to adjust the Dickey-Fuller statistic, making it robust to a wide class of weakly dependent error processes. | The Granger causality test is a statistical hypothesis test that determines whether past values of one time series help predict future values of another, beyond what that series' own past already explains. Introduced by Clive Granger in 1969, it is the standard approach for assessing predictive causality in VAR-based time-series analysis. |
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