st: marginal effects in biprobit

[Janice Compton <comptonj@cc.umanitoba.ca>]

I am using BIPROBIT (Stata9) to estimate an IV model of the type
BIPROB (Y X1 X2 X3) (X1 X2 X3 Z)
where Y, X1 and Z are all binary variables.

I have a comment on an old post and then a question.  First, to find  
marginal effects, I  started by using the program found here:

http://www.stata.com/statalist/archive/2010-02/msg00078.html

The description provided sounds like Average Treatment Effect, but I  
was getting negative results where I had positive coefficients.  After  
a lot of searching, I discovered that ATE is found by either of the  
following procedures:

gen wasx1=x1;
replace x1=1;
predict p1, pmarg1;
replace x1=0;
predict p2, pmarg1;
replace x1=wasx1;
gen ATE1=p1-p2;
sum ATE1;

OR

predict xb1, xb1;
scalar b_x1=x[x1]
gen ate2=0;
replace ate2=norm(xb1+ b_x1) ? norm(xb1) if X1==0;
replace ate2=norm(xb1) ? norm(xb1 ? b_x1) if X1==1;
sum ATE2;

These give the same results.   I think the program from the above post  
looks like a reasonable thing to do but I?m not sure what the  
interpretation is.  Can anyone explain that?

My question relates to the pmarg1 estimate.  If I run the following:
gen wasZ=Z;
replace Z=1;
predict check1, pmarg1;
replace Z=0;
predict check2, pmarg1;
sum check1 check2;

I find that check1 and check2 are identical, so values of the  
instrument do not effect pmarg1.  This suggests to me that pmarg1 is  
estimating the predicted probability of Y at observed values of X1 and  
not estimated values of X1, as I had expected.  Does anyone know how  
to estimate the predicted probability of Y conditional on values for  
the instrument, Z?

Thanks very much for your consideration.

Sincerely,
Janice Compton


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