Dear Statalisters,
I want to extract the generalised residuals from the simple probit model to
use them as additional regressors in a second stage regression--i.e
implementing a variant of Heckman's (1981) solution to the initial
conditions problem.
The generalised residual for the Probit model as defined by Gourieroux et al
(1987) is:
u= {phi(xb).[y-PHI(xb)]}/{PHI(xb).[1-PHI(XB)]}
The way I am thinking of extracting the generalised probit residuals is as
follows:
predict xb, xb
gen norm(xb)=normxb --i.e the cumulative standard normal distr
gen normalden(xb)=normaldenxb -- i.e the standard normal desnity
gen denominator= norxb*[1-normxb]
gen numerator= normaldenxb*[y-normxb] --where y is my binary dependent
variable
Is this right?
Then if I want to extract the error term after estimating a model using
xtprobit (random effects probit) do I follow the same procedure or is this
completely wrong?
Would be most grateful if anyone could help.
Georgios
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