# st: Can I compare the coefficients of one certain variable from two different samples by -suest-?

 From [email protected] To statalist<[email protected]> Subject st: Can I compare the coefficients of one certain variable from two different samples by -suest-? Date Mon, 04 Jan 2010 01:14:14 +0800

```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
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
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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