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| Model ARIMA (Autoregressive Integrated Moving Average)× | Test kointegracije (Johansen / Engle-Granger)× | Test na jedinicu korena Filipsov-Peronov (PP)× | |
|---|---|---|---|
| Oblast | Ekonometrija | Ekonometrija | Ekonometrija |
| Porodica | Regression model | Regression model | Regression model |
| Godina nastanka≠ | 2015 | 1988 | 1988 |
| Tvorac≠ | Box & Jenkins (Box-Jenkins methodology) | Engle & Granger (1987); Johansen (1988) | Peter C. B. Phillips & Pierre Perron |
| Tip≠ | Univariate time-series model | Time-series cointegration test | Unit-root test for stationarity |
| Temeljni izvor≠ | 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 | Johansen, S. (1988). Statistical Analysis of Cointegration Vectors. Journal of Economic Dynamics and Control, 12(2-3), 231-254. DOI ↗ | Phillips, P. C. B., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335–346. DOI ↗ |
| Drugi nazivi≠ | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | Johansen cointegration test, Engle-Granger cointegration test, long-run equilibrium test, Eşbütünleşme Testi (Johansen/Engle-Granger) | PP test, Phillips-Perron unit root test, Phillips-Perron birim kök testi |
| Srodne≠ | 5 | 5 | 4 |
| Sažetak≠ | 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). | The cointegration test examines whether non-stationary time series that each contain a unit root share a stable long-run equilibrium relationship. The single-equation residual approach was introduced by Engle and Granger (1987) and the system-based rank approach by Johansen (1988). | 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. |
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