Thanks for those who responded to my previous
question.
I have an updated question about the non-linear ML
programming and constraints:
Is there way of introducing inequality constraints for
the *parameters* defined by "args" at the beginning of
an ml program?
Thanks,
aslihan arslan
--- n j cox <[email protected]> wrote:
> I don't think anyone needs to look at your code,
> as at least one major problem is evident in
> your footnote.
>
> You say you are using a constraint like
>
> constraint 1 `alpha'>0
>
> Here I take your "like" literally. It so happens
> that -constraint- doesn't complain if you specify
> a constraint in this way -- even if you have not
> specified a local macro called alpha -- but that
> absence of bad news is not good news.
>
> First, your constraint is an inequality and as such
> not linear and thus outwith the reach of
> -constraint-.
> Perhaps -constraint- is a bad name and it really
> should be
> -linearconstraint- but that would get no votes in
> the
> Statavision Name Contest. No matter: it is plainly
> laid out in the help that -constraint- deals with
> linear
> constraints (and none other).
>
> Second, although your constraint won't do, it is
> also
> wrong for another reason. Constraints take their
> identities
> from variable names and possibly equations, and
> -constraint-
> can't work with names that happen to be your private
> names
> for parameters, even if those names are used inside
> your
> program. (You certainly can't expect -constraint- to
> look
> inside your program!)
>
> So far, all negative.
>
> I would forget about the constraint. If your
> specification
> is sensible a positive value for alpha will emerge
> from the
> estimation. If it doesn't you have a signal that the
> apecification
> is suspect in that regard.
>
> Alternatively, just try log(x + 1). The extra degree
> of freedom
> might come in handy. I used to think log(x + 1) was
> a fudge but
> I now regard it more fondly. It's a function that
> goes to 0 as x goes
> to 0 from above and it behaves like log x as x gets
> very large,
> so it is fairly well motivated.
>
> Nick
> [email protected]
>
> aslihan arslan
>
> I am trying to program an ML model to estimate Log
> Cobb Douglas production function where some rhs
> variables have zero values. To make logs defined I
> am
> adding a constant to variables with zeros that needs
> to be estimated too (hence nonlinear MLE). The
> function looks like:
>
> lnY=B0+B1*lnX1+B2*ln (X2+alpha)+epsilon
>
> where X2 is the variable with some zero values, and
> B0, B1, B2 and alpha need to be estimated. I am also
> using a constraint like:
> constraint 1 `alpha'>0.
>
> My program does not work and I am wondering whether
> anybody can help me with this program. I did not
> paste
> the whole program here since it is a little long,
> but
> can send it to those who could help.
> *
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>
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