Conceptually, what is difference between standard error of estimate and standard error of forecast?

[archived_user]

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.

 

[archived_user]

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.

 

[archived_user]

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

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?

 

[archived_user]

cfageist wrote: 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 regression coefficients (betas slopes and intercept).

There: that’s better.

 

[archived_user]

S2000magician wrote:

cfageist wrote: 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 regression coefficients (betas slopes and intercept).

There: that’s better.

Thank you for correcting!

 

[archived_user]

My pleasure.