Dear all
I wonder if anyone has any references, and perhaps Stata applications, that can help me solve a problem like follows:
All models provide the same point estimates, but different standard errors. The benchmark is column 1.
Column 2 combines the effect of female and educ, and adds them into the model. and column 3 simply extracts the effect of female and education from lnwage before estimating the model.
My question is, does anyone know how to correct the standard errors from model 3 or 2, and obtain the "correct" ones from model 1?
I know this could be done using bootstrap methods, but Im trying to see if it can be done in a different way.
For more details on why this. Im revising Robinson's Semiparametric estimator. (see reference below). Im aware about the userwriten command -semipar-. However, the application itself does not provide much detail on how to estimate the standard errors of the nonparametric section.
Looking through the code, it does it in a similar way as the results from column 3, but what we want are the results from column 1.
Thank you in advance.
Robinson, P. M. 1988. Root-n-consistent semiparametric regression. Econometrica 56: 931–954.
I wonder if anyone has any references, and perhaps Stata applications, that can help me solve a problem like follows:
Code:
use http://fmwww.bc.edu/RePEc/bocode/o/oaxaca.dta, clear
* This is the benchmark
reg lnwage educ exper tenure female single
est sto m1
* now, i can create the following variables:
gen double female2=female*_b[female]
gen double educ2=educ*_b[educ]
gen double femaleeduc=female2+educ2
gen double lnwage2=lnwage-female2-educ2
* And estimate this model
reg lnwage femaleeduc exper tenure single
est sto m2
* or this other
reg lnwage2 exper tenure single
est sto m3
est tab m1 m2 m3, se
-----------------------------------------------------
Variable | m1 m2 m3
-------------+---------------------------------------
educ | .08303358
| .00513458
exper | .00895939 .00895939 .00895939
| .00156103 .00155551 .00153964
tenure | .00613849 .00613849 .00613849
| .00187615 .00186656 .00185977
female | -.09382159
| .02490175
single | -.16080964 -.16080964 -.16080964
| .02702903 .02694462 .0269209
femaleeduc | 1
| .05800787
_cons | 2.3408107 2.3408107 2.3408107
| .07085517 .06106525 .02653445
-----------------------------------------------------
legend: b/se
Column 2 combines the effect of female and educ, and adds them into the model. and column 3 simply extracts the effect of female and education from lnwage before estimating the model.
My question is, does anyone know how to correct the standard errors from model 3 or 2, and obtain the "correct" ones from model 1?
I know this could be done using bootstrap methods, but Im trying to see if it can be done in a different way.
For more details on why this. Im revising Robinson's Semiparametric estimator. (see reference below). Im aware about the userwriten command -semipar-. However, the application itself does not provide much detail on how to estimate the standard errors of the nonparametric section.
Looking through the code, it does it in a similar way as the results from column 3, but what we want are the results from column 1.
Thank you in advance.
Robinson, P. M. 1988. Root-n-consistent semiparametric regression. Econometrica 56: 931–954.
