Sorry for not being clear. Actually I think I may have misrepresented
my problem. My main concern is numerical optimization. The function
just happens to be a likelihood. The function is of the form:
L = sum(l)
where
l = log( exp(-2*e*T) * ( (1-a) * e^(B+S) + a*d*exp(-u*T) * (e^B) *
(u+e)^s +
a * (1-d) * exp(-u*T) * (u+e)^B * (u+e)^B
* e^S ) )
subject to the constraints: 0 <= a, d <= 1 and e, u >= 0. T is a
constant.
The summation is over observations of B and S.
I would like to find the values of a,d,e,u that maximize the function
and to estimate their standard errors.
Does this make sense? Can this even be done is Stata?
Cameron
On May 2, 2005, at 7:04 PM, Richard Williams wrote:
At 01:23 PM 5/2/2005 -0400, Cameron Hooper wrote:
Hi
I have a question about using the -ml- command. I've had a look at
the reference manual, but it's a bit over my head. I've seen the
book about ML available from Stata, but I thought someone on this
list might be able to help me out.
Cameron, I don't think it is clear what the question is. Are you
just asking "How do I write a program to do this" or is there a more
specific question you have in mind?
I found that I had pretty good luck copying large chunks of the code
from the ML book verbatim. The tricky part, I think, is writing the
likelihood evaluator and the other code that is unique to your
problem.
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