Hello -
This is not the answer you are looking for, but in my opinion you will have a hard time replicating ARIMA models created in SAS using Excel. The implementation of these kinds of models is more sophisticated than you might expect - and as such difficult to translate to simple Excel statements (in a straightforward manner that is).
Wouldn't it be more appropriate to leave the calculation of the ARIMA model in SAS and export the results to Excel only?
Thanks,
Udo
Hello -
This is not the answer you are looking for, but in my opinion you will have a hard time replicating ARIMA models created in SAS using Excel. The implementation of these kinds of models is more sophisticated than you might expect - and as such difficult to translate to simple Excel statements (in a straightforward manner that is).
Wouldn't it be more appropriate to leave the calculation of the ARIMA model in SAS and export the results to Excel only?
Thanks,
Udo
i have tried replicating the SAS ARIMA (AR model) model in excel and have been successful for the most part... below is the equation that i am using..
below is the equation for p =(1,7,14) model with no d and q terms
Yt = Constant Estimate + ar1*lag1 +ar7*lag7 +ar14*lag14
if the model has other input variables apart from these 3 lags, below is the equation (p =(1,7,14), with input=Temp, with no d and q terms):
Yt = Constant Estimate + ar1*lag1 +ar7*lag7 +ar14*lag14+ num1*Temp-ar1*num1*temp1-ar7*num1*temp7-ar14*num1*temp14
hope this helps.
thanks
Thanks for the Logic, I am struggling with the notations num1. Can you please clarify?
Thanks in advance!
num1,num2 etc. are coefficients for other terms you may include in your arima model.. proc arima lets you add other variables (like regressive) into your model using the followin statement:
estimate input=XYZ;
referrence: http://support.sas.com/documentation/cdl/en/etsug/63348/HTML/default/viewer.htm#etsug_arima_sect012....
hope that helps... if you just have AR term you don't have to worry about the num1,num2 etc.
Thanks for the quick response. It helped me move 1 step ahead, I could replicate ARMA (1,1) with 2 input variables. Now I am trying extend it to a general form of more than 1 AR term (AR 1,2 ) with 2 or more input variables. The below mentioned form does not conform with SAS output.
Yt = Constant Estimate + ar1*lag1 +ar2*lag2 + num1*Temp-ar1*num1*Temp1-ar2*num1*Temp2 {changed the subscripts}
Yt = Constant Estimate + ar1*lag1 +ar2*lag2 + num1*(Temp- ar1*Temp1- ar2*Temp2) {brought num1 forward}
Yt = Constant Estimate + ar1*lag1 +ar2*lag2 + num1*(Temp- (ar1*Temp1+ ar2*Temp2)) {Regression - AR contribution to get the marginal contribution for temp}
I believe the above form suggests that whenever you have AR with input variables, we need to take out the AR interaction out of the input variable to see the marginal contribution of the input variable (Temp in our case). This form does not match with SAS
So i think, there should be a term to take care of the effect of interaction between Ar1 and Ar2 something like A1*Ar2
Yt = Constant Estimate + ar1*lag1 +ar2*lag2 + num1*(Temp-(ar1*temp1 + ar2*temp2 - ar1*temp1*ar2*temp2)
Unfortunately this does not help either.
Nav1, any guidance would be helpful. Thanks a ton!
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