I'm a beginner here, trying to use Stata's ml to estimate a
probit-like equation with non-linear terms. However, whenever I try
using hard-coded independent variables, I get the following error:
initial: log likelihood = -51.292891
alternative: log likelihood = -1877.398
rescale: log likelihood = -48.015768
rescale eq: log likelihood = -48.015768
could not calculate numerical derivatives
flat or discontinuous region encountered
Even when I simplify my system, I still get these errors, so I must be
doing something wrong.
For instance, using the built-in auto data, I try
-----
sysuse auto
capture program drop probit1
program define probit1
args lnf theta1
tempvar p
quietly gen double `p'=norm(`theta1'*mpg)
quietly replace `lnf' = $ML_y1*ln(`p')+(1-$ML_y1)*ln(1-`p')
end
ml model lf probit1 (foreign=) /beta1
ml max
------
This produces the error, even though it is almost identical to the
textbook example for using ml for probit. Specifically, replacing the
relevant lines with the non-hard-coded version
quietly gen double `p'=norm(`theta1')
ml model lf probit1 (foreign=mpg)
works fine. So I assume I am doing something basic wrong in switching
to hard-coded variables.
The reason I need the hard-coded variables is that what I'm really
trying to estimate is closer to
----
capture program drop probit2
program define probit2
args lnf theta1 theta2 theta3 theta4
tempvar p
quietly gen double
`p'=norm(((`theta1'*mpg)^`theta3'+(`theta2'*length)^`theta3')^`theta4')
quietly replace `lnf' = $ML_y1*ln(`p')+(1-$ML_y1)*ln(1-`p')
end
ml model lf probit2 (foreign=) /beta1 /beta2 /gamma1 /gamma2
ml max
----
Which of course produces the same error.
Any help would be greatly appreciated.
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