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st: Adjust after regression involving categorical variables

Subject   st: Adjust after regression involving categorical variables
Date   Wed, 28 Oct 2009 11:44:06 +0000

Dear all,

I am struggling to understand the -adjust- command after regression 
involving categorical variables.  My aim in using -adjust- is to obtain 
the predicted values adjusted for the categorical variable, but I am not 
explicitly interested in the categorical variable and so do not want it 
appearing in the -by()- option of -adjust-.  I have been unable to find 
any examples of this kind of use of -adjust-.  I have reproduced my query 
using the auto dataset below. I am using Stata 10.1 SE.

sysuse auto, clear

** just for this example, assume that rep78 is categorical
xi: regress price weight turn i.rep i.foreign

** output 
price           Coef.           Std. Err.               t       P>t [95% 
Conf.   Interval]
weight          4.243125        .6699849        6.33    0.000   2.903407 
turn            -208.6987       125.9326        -1.66   0.103   -460.5164 
_Irep78_2       822.0914        1691.818        0.49    0.629   -2560.907 
_Irep78_3       710.281         1560.7          0.46    0.651   -2410.531 
_Irep78_4       341.2531        1631.858        0.21    0.835   -2921.848 
_Irep78_5       876.4049        1740.224        0.50    0.616   -2603.387 
_Iforeign_1     3239.838        859.1453        3.77    0.000   1521.871 
_cons           -32.54137       4097.528        -0.01   0.994   -8226.054 

** want the predicted values by foreign - not specifically interested in 
rep78 but wanted to adjust for it, but I am unsure as to how to treat 
** option 1 - set continuous values to mean but leave rep78 as is
adjust weight turn, by(foreign)
** output
 Car type |             xb
 Domestic |     5164.18
  Foreign |     8390.29

** However, you see that 8390.29-5164.18=3226.11, and not 3239.838 as 
predicted by the model above

** option 2 - treat dummies created by -xi- as continuous, and also set 
them to their mean
adjust weight turn  _Irep78_2 _Irep78_3 _Irep78_4 _Irep78_5, by(foreign)

** output
 Car type |             xb
 Domestic |      5160.01
  Foreign |      8399.84

You see that the final -adjust- command gives 8399.84-5160.01=3239.83, as 
given by the regression model above.  So it appears that the second 
treatment of the categorical gives the 'correct' predictions.  However, I 
am struggling to interpret exactly what this means for rep78, and does it 
make sense to set variables that are 0/1 to their mean?

I would be extremely grateful for any assistance with this.

Many thanks,



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