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Re: st: MLE Non-linear Program
From
Jorge Eduardo Pérez Pérez <[email protected]>
To
"[email protected]" <[email protected]>
Subject
Re: st: MLE Non-linear Program
Date
Fri, 19 Nov 2010 17:18:06 -0500
Have you tried -ml search- to look for better initial values?
_______________________
Jorge Eduardo Pérez Pérez
On Fri, Nov 19, 2010 at 2:03 PM, Steven Archambault
<[email protected]> wrote:
>
> Hi all,
> I am running this MLE nonlinear program below. It has trouble
> converging , I guess because of the complexity of having to estimate
> kappa and gamma. Does anybody have any suggestions for improving the
> code, while still getting estimates for gamma and kappa?
> It works fine for estimating kappa only and gamma only. Thanks!
>
> capture program drop simplemle
> program simplemle
> args lnL a0 a1 a2 eta b1 b2 kappa gamma
> tempvar xb sigma lambda lambda2
> quietly gen double `lambda'=exp(`kappa')/(1+exp(`kappa'))
> quietly gen double `lambda2'=exp(`gamma')/(1+exp(`gamma'))
> quietly gen double `xb'=`a0'+ `a1'*lnwaterdist +`a2'*totindexa ///
> + `b1'*(forestsaa+`lambda'*forest_vdc5a)+ `b2'*(ldistkillnov+`gamma'*ltotkill)
>
> quietly gen double `sigma'=exp(`eta')
> quietly replace
> `lnL'=-.5*ln(2*_pi)-.5*ln((`sigma')^2)-.5*(foodindx10-`xb')^2/(`sigma')^2
> end
> ml model lf simplemle (a0:) (a1:) (a2:) (eta:) (b1:) (kappa:) (b2:) (gamma:)
> ml init 1 1 .1 .1 .1 1 1 1 , copy
> ml maximize
>
> I get messages like: (Iteration 70: log likelihood = -3354.8283 (not concave)
> numerical derivatives are approximate
>
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