Hello,
I am using SAS 9.4 (TS1M5). I am trying to figure out how to model my time series data, using segmented regression analysis. I am looking at monthly proportions of kids up to date on vaccination, across a total of 96 months. The purpose of this analysis is to evaluate the impact of a policy change that occurred at month 84 on the rate of kids up to date. Upon visual inspection of the data, it appears that there is an overall decreasing trend. See attached graphic.
I am having trouble understanding how to interpret the Augmented Dickey Fuller test for stationarity. All output is attached. I believe a sound approach is to use proc arima to run this test. Under "trend", Tau and Rho have a p-value<0.05 for lags 0 and 1. My understanding of this is that I should not have an issue with stationarity, as long as I include a trend variable in my model. However, I'm not understanding why Rho and Tau also have p-values<0.05 for lag 0 under "zero mean" and "single mean". Could you please advise? Thank you!
proc arima data=d.late;
identify var=latept stationarity=(adf=(0,1,2));
quit;run;