Comparer des méthodes

	Modèle ARIMA (Autoregressive Integrated Moving Average)	Test de racine unitaire de Phillips-Perron (PP)
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	2015	1988
Auteur d'origine	Box & Jenkins (Box-Jenkins methodology)	Peter C. B. Phillips & Pierre Perron
Type	Univariate time-series model	Unit-root test for stationarity
Source fondatrice	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	Phillips, P. C. B., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335–346.
Alias	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	PP test, Phillips-Perron unit root test, Phillips-Perron birim kök testi
Apparentées	5	4
Résumé	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 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.