BookmarkSubscribeRSS Feed
LNA2021
Calcite | Level 5
data ARIMA_ASTHMA_AB;
Set ASTHMA_ab;
step= (Quarter>='2020-Q1');
ramp= time_after;
if Quarter < '2020-Q1' then ramp=0;
Run;
Trial (1);
proc arima data=ARIMA_ASTHMA_AB;
identify var= Preval_qtr_ResprtyAnti (2,4) crosscorr=(step(2,4) ramp(2,4));
estimate p=2 q=(4) input=(step ramp) method=ml outmodel=AB_AS3;
run; quit;
Trial(2);
proc arima data=ARIMA_ASTHMA_AB;
identify var= Preval_qtr_ResprtyAnti (2,4) crosscorr=(step ramp);
estimate p=1 q=(4) input=(step ramp) method=ml outmodel=AB_AS5;
run; quit;

Hello,

I am using ITS (ARIMA) to study the impact of intervention on drug utilzation.

Based on the above codes: My question is when to use crosscorr= [Step(1,4) Ramp(1,4)] and when to use only crosscorr= [Step Ramp]?

I know in order to choose between different ARIMA models, I need to check SBC and AIC (the lower the better model).

Also, I attached the output for both trial 1 and 3 SAS codes. Could you please advise which model is better and why?

Thanks in advance.

1 REPLY 1
sbxkoenk
SAS Super FREQ

Hello @LNA2021 ,

 

I have moved your question to the

SAS Forecasting and Econometrics board (under the Analytics header).

You will get much better answers here.

 

Note there's a lot of literature on this subject.
And many approaches are taken : ARIMA(X) , State Space Models (SSM), Mixed models , ...

But to re-assure you : PROC ARIMA can definitely be used for interrupted time series analysis.

 

Cheers,

Koen

hackathon24-white-horiz.png

The 2025 SAS Hackathon Kicks Off on June 11!

Watch the live Hackathon Kickoff to get all the essential information about the SAS Hackathon—including how to join, how to participate, and expert tips for success.

YouTube LinkedIn

Discussion stats
  • 1 reply
  • 918 views
  • 0 likes
  • 2 in conversation