stante, als au ch die Nullhypothese der Stationarität mit Konstante und Trend, auf unter 5 %. Signifikanzniveau verwirft. Der ADF-Test bestätigt dieses Er gebnis größtenteils, da er bei den I mistook the p value for ADF test for the KPSS one. auto.arima only uses KPSS by default to specify d. The fact that salests produces a p value of 0.1 would mean that d should be zero (based on the source code). So, probably salests does not end up in that part of the auto.arima code. The ADF test is a statistical test used to determine whether a time-series is stationary. The test uses a null hypothesis that the time-series has a unit root. If the p-value of the test is less than the significance level (usually 0.05), then we reject the null hypothesis and conclude that the time-series is stationary. If you set k=12 and retest, the null of unit root cannot be rejected, > adf.test (electricity, k=12) Augmented Dickey-Fuller Test data: electricity Dickey-Fuller = -1.9414, Lag order = 12, p-value = 0.602 alternative hypothesis: stationary. Assuming that "adf.test" really comes from the "tseries" package (directly or indirectly), the reason tests' performance: ADF, PP and KPSS. 2.1. Review on the Unit Root Tests The Augmented Dickey-Fuller (ADF) Test The ADF test includes extra lagged in terms of the dependent variables to dispense with autocorrelation. One of the weaknesses is that the power of the test is very low. The ADF test is based on an autoregressive process with This research covers the periode for 2000.Q1-2017.Q4, used secondary data which were analyzed using Granger Causality Test and Augmented Dickey Fuller (ADF) and existing data processed by using 3DIqZ.

kpss test vs adf test