st: Referring to coefficients after mlogit

["Korcan Kavusan" <k.kavusan@tilburguniversity.edu>]

Hello all,

I am estimating a multinomial logit model with 3 possible outcomes (0, 1 ,
2). 0 is the base outcome. Mlogit gives two sets of results, one comparing 0
and 1 and the other 0 and 2. So each independent variable has 2
coefficients, one for each comparison. I have a difficulty in correctly
referring to these coefficients after the estimation.

Specifically, Bowen (2010) suggests the code below to compute the value and
significance of a moderating effect for each observation. The code is
written for logit estimation. I want to adapt it to my mlogit model and do
these computations for my two sets of results, but cannot figure out how
tell the code to use the coefficient from the result set that compares 0 and
1 (and then separately from 0-2 comparison later). It is probably a small
technical issue but for me now a real headache. I greatly appreciate any
clue.

The original code for the logit model:

* Estimate logit model for binary dependent variable `dismissed'
logit dismissed X1 X2 X12
predict phat
* Define values used in computing moderating effects
local xb _b[X1]*X1+_b[X2]*X2+_b[X12]*X12+_b[_cons]
local xb0 _b[X1]*X1+_b[X2]*X2+_b[_cons]
local phat (exp(`xb')/(1+exp(`xb')))
local phat0 (exp(`xb0')/(1+exp(`xb0')))
gen phat0 = (exp(`xb0')/(1+exp(`xb0')))
label var phat0 "Predicted probability (model excludes interaction
variable)"
local coef1 (_b[X1]+_b[X12]*X2)
local coef2 (_b[X2]+_b[X12]*X1)
* compute value of each moderating effect at each observation
predictnl total=`phat'*(1-`phat')*(_b[X12]+(1-2*`phat')*`coef1'*`coef2'),
se(se_total)
predictnl structural = `phat0'*(1-`phat0')*((1-2*`phat0')*_b[X1]*_b[X2]),
se(se_structural)
predictnl secondary =
`phat'*(1-`phat')*(_b[X12]+(1-2*`phat')*`coef1'*`coef2') ///
-`phat0'*(1-`phat0')*(1-2*`phat0')*_b[X1]*_b[X2], se(se_secondary)
label var total "Total Moderating Effect"
label var secondary "Secondary Moderating Effect"
label var structural "Structural Moderating Effect"

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