Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Тест KPSS на стаціонарність× | Модель ARIMA (Авторегресійна інтегрована ковзна середня)× | Розширений тест Дікі-Фуллера (ADF) на одиничний корінь× | Тест на коінтеграцію (Йогансен / Енгл-Грейнджер)× | Тест на одиничний корінь Філіпса-Перрона (PP)× | |
|---|---|---|---|---|---|
| Галузь | Економетрика | Економетрика | Економетрика | Економетрика | Економетрика |
| Родина | Regression model | Regression model | Regression model | Regression model | Regression model |
| Рік появи≠ | 1992 | 2015 | 1979 | 1988 | 1988 |
| Автор методу≠ | Kwiatkowski, Phillips, Schmidt & Shin | Box & Jenkins (Box-Jenkins methodology) | David A. Dickey & Wayne A. Fuller | Engle & Granger (1987); Johansen (1988) | Peter C. B. Phillips & Pierre Perron |
| Тип≠ | Stationarity test (reverse of unit-root tests) | Univariate time-series model | Unit-root test for stationarity | Time-series cointegration test | Unit-root test for stationarity |
| Основоположне джерело≠ | Kwiatkowski, D., Phillips, P. C. B., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root. Journal of Econometrics, 54(1–3), 159–178. 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 | Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366a), 427–431. DOI ↗ | 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 ↗ |
| Інші назви≠ | Kwiatkowski-Phillips-Schmidt-Shin test, stationarity test, KPSS durağanlık testi | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | ADF test, Dickey-Fuller test, unit root test, Genişletilmiş Dickey-Fuller testi | 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 |
| Пов'язані≠ | 4 | 5 | 4 | 5 | 4 |
| Підсумок≠ | The KPSS test, introduced by Kwiatkowski, Phillips, Schmidt and Shin in 1992, tests the null hypothesis that a series is stationary against the alternative that it contains a unit root — the reverse of the ADF and Phillips-Perron tests. By flipping the burden of proof, it is designed to be used alongside unit-root tests so that the two can confirm one another and expose ambiguous, borderline cases. | 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 Augmented Dickey-Fuller (ADF) test is the most widely used test for a unit root — that is, for whether a time series is non-stationary and must be differenced before modelling. Introduced by David Dickey and Wayne Fuller in 1979 and extended by Said and Dickey in 1984 to series with higher-order autocorrelation, it regresses the change in the series on its lagged level plus lagged differences and asks whether the lagged-level coefficient is zero. | 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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