st: MLE Non-linear Program

[Steven Archambault <archstevej@gmail.com>]

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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