Skip to content Show To determine if a time series is stationary or has the unit root, three methods can be used: A. The most intuitive way, which is also sufficient in most cases, is to eyeball the ACF (Autocorrelation Function) plot of the time series. The ACF pattern with
a fast decay might imply a stationary series. By testing both the unit root and stationarity, the analyst should be able to have a better understanding about the data nature of a specific time series. The SAS macro below is a convenient wrapper of stationarity tests for many time series in the production environment. (Please note that this macro only works for SAS 9.2 or above.) $\begingroup$
chl 51.7k19 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.
What is the difference between KPSS and ADF test?So in summary, the ADF test has an alternate hypothesis of linear or difference stationary, while the KPSS test identifies trend-stationarity in a series.
What is the difference between ADF test and PP test?Though the PP unit root test is similar to the ADF test, the primary difference is in how the tests each manage serial correlation. Where the PP test ignores any serial correlation, the ADF uses a parametric autoregression to approximate the structure of errors.
How do you interpret the results of ADF?The augmented Dickey–Fuller (ADF) statistic, used in the test, is a negative number. The more negative it is, the stronger the rejection of the hypothesis that there is a unit root at some level of confidence.
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