Dear Stata experts,
I recently learned how to change the degrees of freedom in the regress command using the dof() option (Thanks Trent Mize). This can be very handy when estimating a fixed effects model using the regress command with manually demeaned variables (example from Trent at the end of this post).
I would like a way to change the df like this when estimating an OLS regression using sem. The dof() option is not available, and the two other strategies I tried have not worked yet:
Strategy 1
I can change the df after regress like this:
Unfortunately, this approach does not seem to update the t statistics or confidence intervals. I have not tried that approach yet with sem.
Strategy 2
It also looks like the df can also be changed with the ereturn repost command (see Stata Forum post), but I am not sure how to do that without deleting the b and V matrices.
Does anyone know how to replicate the effect of the dof() option in a way that will work with sem?
Thanks,
Jeremy
I recently learned how to change the degrees of freedom in the regress command using the dof() option (Thanks Trent Mize). This can be very handy when estimating a fixed effects model using the regress command with manually demeaned variables (example from Trent at the end of this post).
I would like a way to change the df like this when estimating an OLS regression using sem. The dof() option is not available, and the two other strategies I tried have not worked yet:
Strategy 1
I can change the df after regress like this:
mata: st_numscalar("e(df_r)", 21761)
Strategy 2
It also looks like the df can also be changed with the ereturn repost command (see Stata Forum post), but I am not sure how to do that without deleting the b and V matrices.
Does anyone know how to replicate the effect of the dof() option in a way that will work with sem?
Thanks,
Jeremy
Code:
webuse union, clear
**************************************************************************
// #0 - Data Management
**************************************************************************
drop if missing(union, age, grade, not_smsa, south, year)
*Calculate person-specific means and demean the variables (subtract the person
*specific mean from each so the new value represents the deviation from
*the subject-specific average)
local ivs "union age grade not_smsa south year"
foreach v in `ivs' {
egen `v'M = mean(`v'), by(idcode)
gen `v'D = `v' - `v'M
}
**************************************************************************
// #1 - Show normal FE model with xtreg
**************************************************************************
xtset idcode
xtreg grade age union i.not_smsa south c.year, fe
est store fixed
**************************************************************************
// #2 - Use all demenaed vars (DV and IVs)
**************************************************************************
*Fixed effects models using all demeaned vars
*Force df to be correct with dof() option (from FE models estimated earlier)
reg gradeD ageD unionD not_smsaD southD yearD, dof(21761)
est store demean
********************************
*Compare the estimates
********************************
esttab fixed demean, nobase noconstant
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