Following on to Ksharp's response that PROC REG is not a good tool for forecasting, what you might do is run PROC REG and get the Durbin-Watson autocorrelation statistic. If it is not extreme, then extrapolation may be good for at least short projections ahead. But to limit the response to a specified range is difficult. You could create the CI on the predicted values, and when it exceeds your range, state that the model is good only out to that point. Another approach could be to apply a differencing schema to your data in an attempt to introduce stationarity. You still have the problem of extrapolation, but autocorrelation should be reduced. Produce the confidence interval at each extrapolated time point. Back-calculate the values from the differenced predicted values, and see how far out you can still meet your pre-specified range. One more thought: I really don't know of any methodology that allows you to limit the range of predicted values for extrapolated independent variables a priori. If someone does, I would be very, very interested in learning about it. Steve Denham
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