Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Тест Филлипса-Перрона (PP) на единичный корень× | Модель ARIMA (авторегрессионная интегрированная скользящая средняя)× | |
|---|---|---|
| Область | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1988 | 2015 |
| Автор метода≠ | Peter C. B. Phillips & Pierre Perron | Box & Jenkins (Box-Jenkins methodology) |
| Тип≠ | Unit-root test for stationarity | Univariate time-series model |
| Основополагающий источник≠ | Phillips, P. C. B., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335–346. DOI ↗ | Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021 |
| Другие названия | PP test, Phillips-Perron unit root test, Phillips-Perron birim kök testi | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli |
| Связанные≠ | 4 | 5 |
| Сводка≠ | 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. | ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015). |
| ScholarGateНабор данных ↗ |
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