Hello - Shared by Technical Support: "This warning typically indicates that one or more of the model parameter estimates is approaching the invertibility or stationarity boundary. You may want to look at your table of parameter estimates to see if any of them are approaching 1 in absolute value.  If they are, then that is an indication of model misspecification. If you have an AR parameter that is nearly 1, then that may be an indication that you need to difference your data.  If you have an MA parameter that is nearly 1, then you may have over-differenced your data. If you think that your model is correctly specified, then you can try adding the NOSTABLE option to the ESTIMATE statement. This allows the procedure's optimization algorithm to iterate outside of the stationarity and invertibility region. In some cases, the final parameter estimates will satisfy the stationarity and invertibility conditions. In this case, you may be able to use the model for forecasting.  However, if the NOSTABLE option is used and the final estimates fall outside the stationarity and invertibility region, then the forecasts from the resulting model could become explosive.  Some information about this topic is available at the following URL: http://support.sas.com/documentation/cdl/en/etsug/63348/HTML/default/viewer.htm#etsug_arima_sect039.htmhttp://support.sas.com/documentation/cdl/en/etsug/68148/HTML/default/viewer.htm#etsug_arima_details29.htm" Thanks, Udo ... View more

Hello - Your data is on daily frequency, but you are asking PROC FORECAST to generate a forecast on yearly frequency. This is why the procedure is complaining. To address the situation you will need to accumulate your data first. Alternatively you may want to consider PROC ESM, which allows you to deal with this challenge in one step. Example (going from monthly to yearly by adding up values - syntax for going from daily to yearly does not change): proc esm data=sashelp.air out=_null_ outfor=outfor plot=forecasts lead=3; id date interval=year accumulate=total; forecast air /method=linear; run; Note that you will need to decide which ESM technique to use - in the example above method=linear seems to make sense. Thanks, Udo ... View more

I tried to use the Inpulse Respoonse Function of the model to get the mentioned forecast intervals: Each random shock is independent, so I can add the variance (sigma**2) weighted by the IRF. Then take the square root. This is also how the simple forecast intervals (Standard Errors) are calculated. Now I only need to be careful with the linear combination. For example the effect of the random shock at lead(2)  - remember, we are calculating std error for lead(7) – is: 0.5*irf(0) + irf(5) Anybody can see a theoretical error in this approach? PROC VARMAX can be used, to get the IRF. Here's a code: proc varmax data=sashelp.citimon;   model CCIUAC / dif(CCIUAC(1)) q=1 p=4 /*6*/ method=ml print=(impulse=(simple accum )) lagmax=6 ;   output out=vforecast lead=12; run; data _null_;   sigma=sqrt(586153.48852); /* number copied here from the output of VARMAX*/   array irf[0:7] (1, 0.33369, 0.29371, 0.46347, 0.23512, 0.21355, 0.22610, 0.14592);/*Impulse Response - numbers copied here from the output of VARMAX*/   array airf[0:7];/*Accumulated Impulse Response*/   airf[0]=1; do i=1 to 7; airf=airf[i-1]+irf;end;/*calculating airf from irf*/   /*Calculating StdErr for forecast lead 2*/   do i=0 to 1;   std_2+airf**2;   end;   std_2=sqrt(std_2)*sigma;   /*Calculating StdErr for forecast lead 7*/   do i=0 to 6;   std_7+airf**2;   end;   std_7=sqrt(std_7)*sigma;   /*Calculating StdErr for the linear combination*/   /*It is similar to a convolution operator, but you sum the squares*/   do i=0 to 6;   if 0<=i<=4 then do;/**/ lincomb+airf**2;   end;   else do;/*5 and 6*/ lincomb+(airf+0.5*airf[i-5])**2;   end;   end;   lincomb=sqrt(lincomb)*sigma;   putlog sigma= std_2= std_7= lincomb=; run; Perhaps SAS/IML can do this with less coding. Thx ... View more

ARIMA does not have a BACK= option in the ESTIMATE statement.  Therefore, the parameter estimation uses all the available data.  The BACK= option in the FORECAST statement withholds the specified number of observations from the end of the data (that is, these observations are not used in forecasting calculations).  It is easier to explain this with an example:  data d1;    set sashelp.air;    logair=log(air); run; proc arima data==d1;    i var=logair(1 12) noprint;    e q=(1)(12) noint method=ml;    f back=12 lead=24;    f lead=24; quit; proc ucm data=d1;    model logair;    irregular q=1 sq=1 s=12;    deplag lags=(1)(12) phi=1 1 noest;    estimate;    forecast back=12 lead=24; run; The ARIMA forecast output for the first forecast statement (with back=12) should be essentially the same as the UCM forecast output (with back=12 in the forecast statement).  In ARIMA, the second forecast statement uses all the 144 measurements, whereas the first forecast statement uses the first 132 measurements. Hope this helps. ... View more

Carlos - Yes - the only thing I'd like to add is that you can define a user specified model repository yourself using the GUI. But of course you will need to decide for yourself which independent series or events to include. If you have a lot of potential candidates for independent series, you may want to preprocess your data trying to identify significant series yourself. One way to go about this is to do a similarity analysis of input combined with a cluster analysis. Purpose would be to identify similar patterns in your input series beforehand. This thread: https://communities.sas.com/message/172179#172179 might be of interest. Thanks, Udo ... View more

Hello - Yes, please contact Technical Support directly - you may want to use their online form: http://support.sas.com/ctx/supportform/createForm Thanks, Udo ... View more