手法を比較

	ARIMA（自己回帰和分移動平均）モデル	拡張ディッキー・フラー（ADF）単位根検定	Phillips-Perron (PP) 単位根検定
分野	計量経済学	計量経済学	計量経済学
系統	Regression model	Regression model	Regression model
提唱年	2015	1979	1988
提唱者	Box & Jenkins (Box-Jenkins methodology)	David A. Dickey & Wayne A. Fuller	Peter C. B. Phillips & Pierre Perron
種類	Univariate time-series model	Unit-root test for stationarity	Unit-root test for stationarity
原典	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.	Phillips, P. C. B., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335–346.
別名	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	ADF test, Dickey-Fuller test, unit root test, Genişletilmiş Dickey-Fuller testi	PP test, Phillips-Perron unit root test, Phillips-Perron birim kök testi
関連	5	4	4
概要	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 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.