i have a time series variable which i checked it for stationarity using ADF test and arima proc in SAS Show proc arima data=&td19; identify var=interest_rate stationarity=(adf=4); run; quit; Below is the ADF test and trend & correlation analysis i started from the table from the bottom (trend model) and using the Tau compared it with 5% until to reject the null. i concluded that the series is stationary since 0.0315<0.05 zero mean model However, when i see the ACF/PACF, based on my understanding, the series in not stationary. This is because the ACF decays slowly My questions are : is this series stationary or not?  $\begingroup$
chl 51.6k19 gold badges210 silver badges370 bronze badges asked Oct 28, 2013 at 5:34
$\endgroup$ 3 $\begingroup$ Look at the ADF Unit Root Test section. If your data is a random walk with drift, then it will be under the type 'Single Mean'. For the ADF test, H0: Non-stationary Ha: Stationary if P-value < 0.05, you reject the null hypo (H0) and conclude that data series is stationary. It should be as you already differenced the data once. Under 'Pr < Rho' which stands for the P-value of your Rho (autocorrelation), it is 0.0129 and <0.0001 thus, we reject the null hypo and conclude that the data is stationary. answered Nov 4, 2013 at 3:44
$\endgroup$ 1 How do you check stationarity in SAS?proc arima data=b; identify var=u stationarity=(adf=0); run; identify var=u stationarity=(pp=0); run; quit; The first IDENTIFY statement performs the ADF unit root tests for u. The stationarity test results are shown in Output 7.8. 8.
How many lags should I use in ADF test?If you have quarterly data, test up to 4 lags. If you have monthly data test up to 12 lags. If the ADF test comes up with a high tau value and a resulting low p-value, you can reject the null hypothesis that the variable is non-stationary.
Why ADF test is better than DF test?The primary differentiator between the two tests is that the ADF is utilized for a larger and more complicated set of time series models. The augmented Dickey-Fuller statistic used in the ADF test is a negative number. The more negative it is, the stronger the rejection of the hypothesis that there is a unit root.
What is the difference between DickeyThe augmented dickey- fuller test is an extension of the dickey-fuller test, which removes autocorrelation from the series and then tests similar to the procedure of the dickey-fuller. When we make a model for forecasting purposes in time series analysis, we require a stationary time series for better prediction.
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Sas stationarity test 방법 adf
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