Confronta i metodi

	Modello ARIMA (Autoregressive Integrated Moving Average)	Test di radice unitaria aumentato di Dickey-Fuller (ADF)	Test di cointegrazione (Johansen / Engle-Granger)	Test di Stazionarietà KPSS
Campo	Econometria	Econometria	Econometria	Econometria
Famiglia	Regression model	Regression model	Regression model	Regression model
Anno di origine	2015	1979	1988	1992
Ideatore	Box & Jenkins (Box-Jenkins methodology)	David A. Dickey & Wayne A. Fuller	Engle & Granger (1987); Johansen (1988)	Kwiatkowski, Phillips, Schmidt & Shin
Tipo	Univariate time-series model	Unit-root test for stationarity	Time-series cointegration test	Stationarity test (reverse of unit-root tests)
Fonte seminale	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.	Johansen, S. (1988). Statistical Analysis of Cointegration Vectors. Journal of Economic Dynamics and Control, 12(2-3), 231-254.	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.
Alias	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)	Kwiatkowski-Phillips-Schmidt-Shin test, stationarity test, KPSS durağanlık testi
Correlati	5	4	5	4
Sintesi	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 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.