השוואת שיטות

	מבחן קיי.פי.אס.אס (KPSS) לנייחות	מודל ARIMA (Autoregressive Integrated Moving Average)	מבחן שורש יחידה מורחב של דיקי-פולר (ADF)	מבחן קואינטגרציה (יוהנסן / אנגל-גריינג'ר)	מבחן שורש יחידה פיליפס-פררון (PP)
תחום	אקונומטריקה	אקונומטריקה	אקונומטריקה	אקונומטריקה	אקונומטריקה
משפחה	Regression model	Regression model	Regression model	Regression model	Regression model
שנת המקור	1992	2015	1979	1988	1988
הוגה השיטה	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
סוג	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
מקור מכונן	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.	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.	Phillips, P. C. B., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335–346.
כינויים	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
קשורות	4	5	4	5	4
תקציר	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.