st: Clarify, tfunc(exp)

[Renzo Carriero <renzo.carriero@unito.it>]

Dear Statalist users,

I have a question about the tfunc(exp) option of simqi command in King's 
et al. Clarify command suite. I can't understand how this option exactly 
compute exponentiated expected value. My depvar is a log ratio, say 
y=ln(a/b). My linear regression model contains a 4-category nominal 
variable (indicated by 3 dummies) plus covariates. When I compute mean 
predicted values on the log scale, results yielded by simqi coincide 
with those yielded by Stata's command adjust. On the contrary, when I 
ask for expected values in the original scale, that is exp(y), they 
markedly differ. It seems that Stata (adjust command with the "exp" 
option) exponentiates the mean expected value, such as exp[E(y|x)], 
while simqi does not. So what is exactly the way in which simqi 
transforms expected values in the original scale?
To help, I add below a simplified example of what I mean using Stata's 
auto dataset

Many thanks
Renzo

--
Renzo Carriero
Dipartimento di Scienze Sociali
via S. Ottavio 50
10124 Torino - Italy
+390116702658 (office)
+393898160069 (mobile)
+390116702612 (fax)

sysuse auto.dta
g lny=ln(price/mpg)/*generate a log var similar to mine*/
xtile x=length, nquantile(4) /*generate a 4 category variable from 
length variable"
tab x, gen(x)/*generate 4 dummies*/
reg lny x2-x4/*x1 is omitted as reference  category*/

      Source |       SS       df       MS                      Number 
of obs =      74
-------------+------------------------------                       F(  
3,    70) =   18,48
       Model |  10,7554461     3  3,58514872           Prob>  F      =  
0,0000
    Residual |   13,580584    70  ,194008343           R-squared     =  
0,4420
-------------+------------------------------           Adj R-squared =  
0,4180
       Total |  24,3360302    73  ,333370277           Root MSE      =  
,44046

------------------------------------------------------------------------------ 

         lny |      Coef.   Std. Err.      t    P>|t|     [95% Conf. 
Interval]
-------------+---------------------------------------------------------------- 

          x2 |    ,576453   ,1461643     3,94   0,000     ,2849374    
,8679685
          x3 |    ,635685   ,1376187     4,62   0,000     ,3612132    
,9101569
          x4 |   1,050355   ,1437037     7,31   0,000     ,7637466    
1,336962
       _cons |   5,078354   ,0961171    52,84   0,000     4,886655    
5,270054
------------------------------------------------------------------------------ 


adjust x2=0 x3=0 x4=0 /*compute expected value for omitted category 1*/

     Dependent variable: lny     Command: regress
Covariates set to value: x2 = 0, x3 = 0, x4 = 0
------------------------------------------------------------------------------------------------------ 


----------------------
      All |         xb
----------+-----------
          |    5,07835
----------------------
     Key:  xb  =  Linear Prediction


adjust x2=0 x3=0 x4=0 , exp /*compute expected value for category 1 in 
the original scale (price/mpg)*/

     Dependent variable: lny     Command: regress
Covariates set to value: x2 = 0, x3 = 0, x4 = 0
------------------------------------------------------------------------------------------------------ 


----------------------
      All |    exp(xb)
----------+-----------
          | *160,51 *
----------------------
     Key:  exp(xb)  =  exp(xb)

estsimp reg lny x2-x4/*replicate analysis with estsimp*/
setx 0 /*set x1=1*/
simqi
*this output omitted, it is roughly equal to that of adjust without exp 
option
simqi, tfunc(exp)

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. 
Interval]
---------------------------+-------------------------------------------------- 

               E[exp(lny)] | *177,6523 *17,95269     145,2497     215,657

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