Dear Statalisters,
I would like to calculate the average marginal effects from a recursive bivariate probit. Austin Nichols suggests three methods in his presentation "causal inference for binary regression". The first method computes slightly different AME compared to the other two procedures.
A previous thread on this topic suggested to use the following method to calculate the AME, which computes the predicted conditional probabilities of success using the bivariate predicted probabilities and the univariate predicted marginal probability. The results in this case, however, are completely different from the ones obtained using one of the three methods suggested above.
Isn't this second piece of code equivalent to ATE1 above? Any suggestion on what is the best way to proceed?
Thank you in advance
I would like to calculate the average marginal effects from a recursive bivariate probit. Austin Nichols suggests three methods in his presentation "causal inference for binary regression". The first method computes slightly different AME compared to the other two procedures.
Code:
biprobit (y=x R) (R=x A) margins, dydx(R) predict(pmarg1) force loc ATEm=el(r(b),1,1) predict double xb2, xb2 preserve ren R TR g R=0 predict double p0, pmarg1 predict double xb0, xb1 replace R=1 predict double p1, pmarg1 predict double xb1, xb1 g double dp=p1-p0 su dp, mean loc ATE1=r(mean) su dp if TR==1, mean loc TOT1=r(mean) loc r=e(rho) gen double pdx=(binormal(xb1,xb2,`r')-binormal(xb0,xb2,`r'))/normal(xb2) if TR==1 su pdx, mean loc TOT2=r(mean) qui replace pdx=normal(xb1)-normal(xb0) su pdx, mean loc ATE2=r(mean)
A previous thread on this topic suggested to use the following method to calculate the AME, which computes the predicted conditional probabilities of success using the bivariate predicted probabilities and the univariate predicted marginal probability. The results in this case, however, are completely different from the ones obtained using one of the three methods suggested above.
Code:
biprobit (y=x R) (R=x A) gen wasr=R replace R=1 predict p1a if e(sample), p11 predict p1b if e(sample), p10 predict p1c if e(sample), p01 predict p1d if e(sample), p00 gen p1=p1a/(p1a+p1c) replace R=0 predict p0a if e(sample), p11 predict p0b if e(sample), p10 predict p0c if e(sample), p01 predict p0d if e(sample), p00 gen p0=p0b/(p0b+p0d) replace R=wasr gen dp=p1-p0 sum dp
Thank you in advance
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