I am using proc arima in SAS 9.4 to produce a forecast using a previously calibrated model, but it is throwing an error as if it is trying to calibrate the model itself : ERROR: There is not enough data to fit the model sample data: data inputs;
input x var1 var2 var3 var4 var5;
datalines;
20 5 2 4 5 4
25 12 56 13 44 4
20 5 2 4 5 4
25 12 56 13 44 4
20 5 2 4 5 4
25 12 56 13 44 4
. 2 5 6 5 4
; failing version: proc arima;
identify
data = inputs
var = x
crossCorr = ( var1 var2 var3 var4 var5 )
noPrint;
estimate
p = 1 input = ( var1 var2 var3 var4 var5 )
ar = 0.9
initVal = ( 0.1$var1 0.2$var2 0.3$var3 0.4$var4 0.4$var5 )
noint
noEst /* Using noEst so should not need to do any estimation and short data-set should not be a problem */
method=ml
noprint
;
forecast lead=1 out=outputs noOutAll noprint;
quit; If I remove the final variable from the model, it works fine: proc arima;
identify
data = inputs
var = x
crossCorr = ( var1 var2 var3 var4 )
noPrint;
estimate
p = 1 input = ( var1 var2 var3 var4 )
ar = 0.9
initVal = ( 0.1$var1 0.2$var2 0.3$var3 0.4$var4 )
noint
noEst /* Using noEst so should not need to do any estimation and short data-set should not be a problem */
method=ml
noprint
;
forecast lead=1 out=outputs noOutAll noprint;
quit; I can also get it to 'work' by adding one more value to the data. However, this shouldn't be necessary when the model is already calibrated (using much more data). I've checked the SAS documentation to see if there are any flags to prevent the unnecessary check that causes this error but none of them helped. Cross-posted on stack-overflow
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