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st: Using ml with hard-coded variables.


From   "Nick B." <3kdo02s@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   st: Using ml with hard-coded variables.
Date   Thu, 17 May 2007 18:54:41 -0400

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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