Translation of examples in Stata tip 87 to Stata version 10 or less

Maarten L. Buis

Abstract

The examples in the article Stata tip 87: Interpretation of interactions in non-linear models use two features that are new in Stata 11, factor variables and the margins command. However the logic of the argument in this article also applies to earlier versions of Stata (and other statistical packages). Below is Stata code for the same examples that can be run in earlier versions of Stata. 


. sysuse nlsw88, clear
(NLSW, 1988 extract)


. gen byte high_occ = occupation < 3 if occupation < .
(9 missing values generated)


. gen byte black = race == 2 if race < .


. drop if race == 3
(26 observations deleted)


. gen byte baseline = 1


. 
. // manually create the interection term 
. gen blXcoll = black*collgrad


. 
. logit high_occ black collgrad blXcoll baseline, or nocons nolog


Logistic regression                               Number of obs   =       2211
                                                  Wald chi2(4)    =     504.62
Log likelihood = -1199.4399                       Prob > chi2     =     0.0000


------------------------------------------------------------------------------
    high_occ | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       black |   .4194072   .0655069    -5.56   0.000     .3088072    .5696188
    collgrad |   2.465411    .293568     7.58   0.000     1.952238    3.113478
     blXcoll |   1.479715   .4132536     1.40   0.161     .8559637    2.558003
    baseline |   .3220524   .0215596   -16.93   0.000     .2824512    .3672059
------------------------------------------------------------------------------


. 
. // use -adjust- instead of -margins-
. adjust, by(collgrad black) exp


-------------------------------------------------------------------------------
     Dependent variable: high_occ     Equation: high_occ     Command: logit
   Variables left as is: baseline, blXcoll
-------------------------------------------------------------------------------


-----------------------------------
                 |      black
college graduate |       0        1
-----------------+-----------------
not college grad | .322052  .135071
    college grad | .793991  .492754
-----------------------------------
     Key:  exp(xb)


. 
. // -adjust- cannot leave its results behind as if it where an
. // estimation command, so we need to compute the expected odds
. // and their differences using -nlcom- instead of leaving the
. // expected odds behind and use -lincom- to compute the differences
. 
. nlcom ( white: exp(_b[baseline]+_b[collgrad] ) -                        ///
>                exp(_b[baseline] )                  )                    ///
>       ( black: exp(_b[baseline]+_b[black]+_b[collgrad]+_b[blXcoll]) -   ///
>                exp(_b[baseline]+_b[black])                            ) ///
> 


       white:  exp(_b[baseline]+_b[collgrad] ) -                               
>         exp(_b[baseline] )
       black:  exp(_b[baseline]+_b[black]+_b[collgrad]+_b[blXcoll]) -          
>         exp(_b[baseline]+_b[black])


------------------------------------------------------------------------------
    high_occ |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       white |    .471939    .081106     5.82   0.000     .3129742    .6309038
       black |   .3576825   .1049933     3.41   0.001     .1518994    .5634656
------------------------------------------------------------------------------



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