I have got two time series and I want to evaluate a VAR model. For this, it is necessary that both time series are stationary.
R, I have found periodicity with the function
spectrum in the lag 16 and 98 in both time series and lots of others in the goal-function
y-Data you see in the second picture.
Obviously, both time series are seasonal. In my opinion, the consequence of this is, that the time series both are nonstationary, because the expected value of the time series depends on time.
Now I check stationarity with the ADF and the KPSS tests, and both seem to suggest stationarity.
adf.test(Data) Augmented Dicke y-Fuller Test data: Data Dickey-Fuller = -3.4722, Lag order = 7, p-value = 0.04498 alternative hypothesis: stationary
kpss.test(Data, null="L", lshort="F") KPSS Test for Level Stationarity data: Data KPSS Level = 0.03706, Truncation lag parameter = 15, p-value = 0.1
Question: Why do they indicate stationarity?