Hi,
Based on the updated information, if you let Y=traffic_In, x1=Total_Wifi_Users and x2=Num_Trans_Rstrnt3, then you can specify your model in PROC ARIMA as:
proc arima data=dsname;
identify var=y(1,7) crosscorr=( x1(1,7) x2(1,7));
estimate p=(1) q=(1)(7)
input=( (1)(7)/(1) x1 (1)/(1)(7) x2) method=ml;
run;
quit;
I believe JMP uses maximum likelihood estimation by default, which is why I added the METHOD=ML option in the ESTIMATE statement. For more details on the syntax for the INPUT= option when fitting a transfer function model in PROC ARIMA, please see the following documentation link:
http://support.sas.com/documentation/cdl/en/etsug/68148/HTML/default/viewer.htm#etsug_arima_details24.htm
It is possible that the parameter estimates computed by PROC ARIMA might differ from those obtained in JMP due to differences in the optimization algorithm, starting values, convergence criteria, etc. However, for a model that fits the data well, the estimates should be relatively close.
I hope this helps!
DW
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