General question: types of explanatory variables one can use in forecasting

[sassygrl]

Hello,

I am trying to forecast future web sales for my company. A subset of that is "keycoded" revenue that can be traced back to particular advertising campaigns. The marketing dept. produces forecasts for keycoded revenue, and I would like to use those as an explanatory variable to predict overall web sales.

My concern is this: there is almost a perfect relationship between *actual* keycoded revenue and *actual* overall web sales. Obviously, I won't have the benefit of knowing future actuals when making forecasts; I will have *forecasted* keycoded revenue instead.

So my question is, would you prefer to use historical actuals or historical forecasts of keycoded revenue when developing the forecasting model?

I hope this wasn't too confusing. Thank you!

[udo_sas]

Hello -
I'm not completely sure I'm getting your point, but here are some thoughts anyway:
when using explanatory variables in your models it will be crucial to have future values of these variables in order to calculate the forecast for the dependent variable.
As such I think I would go with actual numbers, as they reflect what was happening in fact - not the "wishful thinking" of ours.
What you will need to do is to create forecasts for your independent variable first (if you don't have future estimates - or use the forecasts of your marketing folks as future values) and then create the prediction of the dependent variable.
In fact, you might consider doing a what-if analysis, i.e. testing what happens if you change the future values of your dependent variables.
Note that this is an out-of-the-box feature of SAS Forecast Studio - which is part of SAS Forecast Server.
Thanks!
Udo