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

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.

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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.

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?

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.

Simplify the complicated side; don't complify the simplicated side.

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http://financialexamhelp123.com/

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!

My pleasure.

Simplify the complicated side; don't complify the simplicated side.

Financial Exam Help 123: The place to get help for the CFA® exams
http://financialexamhelp123.com/