방법 비교

	필립스-페론(PP) 단위근 검정	ARIMA (Autoregressive Integrated Moving Average) 모형	증강된 Dickey-Fuller (ADF) 단위근 검정	KPSS 정상성 검정
분야	계량경제학	계량경제학	계량경제학	계량경제학
계열	Regression model	Regression model	Regression model	Regression model
기원 연도	1988	2015	1979	1992
창시자	Peter C. B. Phillips & Pierre Perron	Box & Jenkins (Box-Jenkins methodology)	David A. Dickey & Wayne A. Fuller	Kwiatkowski, Phillips, Schmidt & Shin
유형	Unit-root test for stationarity	Univariate time-series model	Unit-root test for stationarity	Stationarity test (reverse of unit-root tests)
원전	Phillips, P. C. B., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335–346.	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.	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.
별칭	PP test, Phillips-Perron unit root test, Phillips-Perron birim kök testi	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	ADF test, Dickey-Fuller test, unit root test, Genişletilmiş Dickey-Fuller testi	Kwiatkowski-Phillips-Schmidt-Shin test, stationarity test, KPSS durağanlık testi
관련	4	5	4	4
요약	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.	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 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.