Stata Code Fragment
Comparing Regression Coefficients Across Groups using Suest

The FAQ at http://www.ats.ucla.edu/stat/stata/faq/compreg3.htm shows how you can compare regression coefficients across three groups using xi and by forming interactions.  This can also be done using suest as shown below.

use http://www.ats.ucla.edu/stat/stata/faq/compreg3

regress weight height if age==1

      Source |       SS       df       MS              Number of obs =      10
-------------+------------------------------           F(  1,     8) =    0.24
       Model |   42.657257     1   42.657257           Prob > F      =  0.6396
    Residual |  1440.94274     8  180.117843           R-squared     =  0.0288
-------------+------------------------------           Adj R-squared = -0.0927
       Total |      1483.6     9  164.844444           Root MSE      =  13.421

------------------------------------------------------------------------------
      weight |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      height |  -.3768309   .7743341    -0.49   0.640    -2.162449    1.408787
       _cons |   170.1664   49.43018     3.44   0.009     56.18024    284.1526
------------------------------------------------------------------------------

est store age1

regress weight height if age==2

      Source |       SS       df       MS              Number of obs =      10
-------------+------------------------------           F(  1,     8) =  359.81
       Model |  1319.56112     1  1319.56112           Prob > F      =  0.0000
    Residual |  29.3388815     8  3.66736019           R-squared     =  0.9782
-------------+------------------------------           Adj R-squared =  0.9755
       Total |      1348.9     9  149.877778           Root MSE      =   1.915

------------------------------------------------------------------------------
      weight |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      height |   2.095872    .110491    18.97   0.000      1.84108    2.350665
       _cons |   -2.39747   7.053272    -0.34   0.743    -18.66234     13.8674
------------------------------------------------------------------------------

est store age2

regress weight height if age==3

      Source |       SS       df       MS              Number of obs =      10
-------------+------------------------------           F(  1,     8) =  669.93
       Model |  3882.53627     1  3882.53627           Prob > F      =  0.0000
    Residual |  46.3637317     8  5.79546646           R-squared     =  0.9882
-------------+------------------------------           Adj R-squared =  0.9867
       Total |      3928.9     9  436.544444           Root MSE      =  2.4074

------------------------------------------------------------------------------
      weight |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      height |   3.189727   .1232367    25.88   0.000     2.905543    3.473912
       _cons |   5.601677   8.930197     0.63   0.548    -14.99139    26.19475
------------------------------------------------------------------------------

est store age3

suest age1 age2 age3

Simultaneous results for age1, age2, age3
                                                            Obs      =      30

------------------------------------------------------------------------------
             |               Robust
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
age1_mean    |
      height |  -.3768309    .496919    -0.76   0.448    -1.350774    .5971124
       _cons |   170.1664   31.30796     5.44   0.000      108.804    231.5289
-------------+----------------------------------------------------------------
age1_lnvar   |
       _cons |   5.193611   .3232765    16.07   0.000     4.560001    5.827222
-------------+----------------------------------------------------------------
age2_mean    |
      height |   2.095872   .0862164    24.31   0.000     1.926891    2.264853
       _cons |   -2.39747   5.668326    -0.42   0.672    -13.50719    8.712246
-------------+----------------------------------------------------------------
age2_lnvar   |
       _cons |   1.299472   .2573396     5.05   0.000     .7950958    1.803848
-------------+----------------------------------------------------------------
age3_mean    |
      height |   3.189727   .1008442    31.63   0.000     2.992077    3.387378
       _cons |   5.601677   7.218938     0.78   0.438    -8.547181    19.75054
-------------+----------------------------------------------------------------
age3_lnvar   |
       _cons |   1.757076   .2637826     6.66   0.000     1.240072     2.27408
------------------------------------------------------------------------------

test [age1_mean]height=[age2_mean]height

 ( 1)  [age1_mean]height - [age2_mean]height = 0

           chi2(  1) =   24.04
         Prob > chi2 =    0.0000

test [age2_mean]height=[age3_mean]height, accum

 ( 1)  [age1_mean]height - [age2_mean]height = 0
 ( 2)  [age2_mean]height - [age3_mean]height = 0

           chi2(  2) =  102.25
         Prob > chi2 =    0.0000

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