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" If can, why the coeffiecients in -suest- are different from independent
-reg- no matter whether I take vce(robust) or not ?"
See [R], page 1802, first "technical note"
" What does "_lnvar" mean?"
See page 1803:
" regress does not include its ancillary parameter, the residual variance,
in its coefficient vector
and (co)variance matrix. Moreover, while the score option is allowed with
predict after regress,
a score variable is generated for the mean but not for the variance
parameter. suest contains special
code that assigns the equation name mean to the coefficients for the mean,
adds the equation lnvar
for the log variance, and computes the appropriate two score variables
itself."
HTH
Martin
-----Original Message-----
From: [email protected]
[mailto:[email protected]] On Behalf Of [email protected]
Sent: Sonntag, 3. Januar 2010 18:14
To: statalist
Subject: st: Can I compare the coefficients of one certain variable from two
different samples by -suest-?
Dear statalists,
Can I compare the coefficients of one certain variable from two different
samples by -suest-?
If can, why the coeffiecients in -suest- are different from independent
-reg- no matter whether I take vce(robust) or not ? Which one should I take
to report?
What does "_lnvar" mean?
webuse income,clear
.
. regress inc edu exp if male
Source | SS df MS Number of obs =
110
-------------+------------------------------ F( 2, 107) =
20.05
Model | 639.919043 2 319.959521 Prob > F =
0.0000
Residual | 1707.31485 107 15.9562136 R-squared =
0.2726
-------------+------------------------------ Adj R-squared =
0.2590
Total | 2347.2339 109 21.5342559 Root MSE =
3.9945
----------------------------------------------------------------------------
--
inc | Coef. Std. Err. t P>|t| [95% Conf.
Interval]
-------------+--------------------------------------------------------------
--
edu | 1.841002 .383369 4.80 0.000 1.081018
2.600986
exp | 1.590727 .3569439 4.46 0.000 .8831278
2.298327
_cons | 1.783822 .3818906 4.67 0.000 1.026769
2.540876
----------------------------------------------------------------------------
--
.
. estimates store Male
.
.
. regress inc edu exp if !male
Source | SS df MS Number of obs =
167
-------------+------------------------------ F( 2, 164) =
43.30
Model | 1418.47853 2 709.239266 Prob > F =
0.0000
Residual | 2686.09306 164 16.3786162 R-squared =
0.3456
-------------+------------------------------ Adj R-squared =
0.3376
Total | 4104.57159 166 24.7263349 Root MSE =
4.0471
----------------------------------------------------------------------------
--
inc | Coef. Std. Err. t P>|t| [95% Conf.
Interval]
-------------+--------------------------------------------------------------
--
edu | 2.475213 .3160483 7.83 0.000 1.851165
3.099261
exp | 1.354081 .3043211 4.45 0.000 .7531885
1.954974
_cons | 1.250719 .3132966 3.99 0.000 .6321043
1.869334
----------------------------------------------------------------------------
--
. . estimates store Female.
.
.
. suest Male Female
Simultaneous results for Male, Female
Number of obs =
277
----------------------------------------------------------------------------
--
| Robust
| Coef. Std. Err. z P>|z| [95% Conf.
Interval]
-------------+--------------------------------------------------------------
--
Male_mean |
edu | 1.841002 .3911029 4.71 0.000 1.074454
2.607549
exp | 1.590727 .3320187 4.79 0.000 .9399827
2.241472
_cons | 1.783822 .3829948 4.66 0.000 1.033166
2.534478
-------------+--------------------------------------------------------------
--
Male_lnvar |
_cons | 2.769848 .1328349 20.85 0.000 2.509497
3.0302
-------------+--------------------------------------------------------------
--
Female_mean |
edu | 2.475213 .3093986 8.00 0.000 1.868803
3.081623
exp | 1.354081 .2982058 4.54 0.000 .7696084
1.938554
_cons | 1.250719 .3122779 4.01 0.000 .638666
1.862773
-------------+--------------------------------------------------------------
--
Female_lnvar |
_cons | 2.795977 .0976384 28.64 0.000 2.604609
2.987344
----------------------------------------------------------------------------
--
. test [Male_mean]edu = [Female_mean]edu
( 1) [Male_mean]edu - [Female_mean]edu = 0
chi2( 1) = 1.62
Prob > chi2 = 0.2035
Many thanks for any help!
Best regards,
Rose.
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