I know that SEE is used to calculate SEF, which, in turn, is used to construct confidence intervals for the predicted value. I’m looking for more of a conceptual explanation of the difference between what is measured by the two.

SEE = Standard error (standard deviation of errors terms). The more variation there, the less precise is your model overall.

SEF = Addresses the variability in your correlation coefficients (betas).

Both cover the two types of uncertainties you have in your regression model.

When you say “correlation coefficients”, do you mean regression coefficients (i.e., b0, b1, etc.)? If so, I thought that standard error of regression coefficients are not the same as standard error of forecast?

There: that’s better.

Thank you for correcting!

My pleasure.