Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Тест Филлипса-Перрона на единичный корень× | Тест причинности по Грейнджеру× | |
|---|---|---|
| Область | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1988 | 1969 |
| Автор метода≠ | Peter C. B. Phillips and Pierre Perron | Clive W. J. Granger |
| Тип≠ | Hypothesis test (unit root) | Causality test (F-test on VAR) |
| Основополагающий источник≠ | 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 ↗ |
| Другие названия | PP test, PP unit root test, Phillips-Perron test, nonparametric unit root test | Granger test, GC test, predictive causality test, Granger non-causality test |
| Связанные | 5 | 5 |
| Сводка≠ | 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. |
| ScholarGateНабор данных ↗ |
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