Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Phillipsův-Perronův test jednotkového kořene× | Model ARIMA (Autoregressive Integrated Moving Average)× | |
|---|---|---|
| Obor | Ekonometrie | Ekonometrie |
| Rodina | Regression model | Regression model |
| Rok vzniku≠ | 1988 | 1970 |
| Tvůrce≠ | Peter C. B. Phillips and Pierre Perron | George Box and Gwilym Jenkins |
| Typ≠ | Hypothesis test (unit root) | Time series forecasting model |
| Původní zdroj≠ | 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. (1970). Time Series Analysis: Forecasting and Control. Holden-Day. link ↗ |
| Další názvy | PP test, PP unit root test, Phillips-Perron test, nonparametric unit root test | ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q) |
| Příbuzné≠ | 5 | 6 |
| Shrnutí≠ | 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 ARIMA(p,d,q) model is the standard workhorse for univariate time series forecasting. It combines autoregressive terms (past values), differencing to induce stationarity, and moving average terms (past shocks) into a unified linear framework. Developed by Box and Jenkins (1970), it remains one of the most widely applied models in econometrics and applied statistics. |
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