I am not sure exactly what you are trying
to do here. It is usually better to set
up a log likelihood function directly. What
you are logging here does not have the usual
form of a likelihood function.
Nick
[email protected]
Jun Xu
> I am not sure if some expert could give me some hint about
> where to go. Here
> I am trying to estimate a logit with an added expotential
> distributed random
> error (with mean of 5). I am using simualted mle with 100
> replications, but
> cannot get it converged. Not sure if I had a correct setup.
>
>
> set trace off
> set more off
> cap program drop my_ll
> program my_ll
> version 8.2
> args lnf xb
> tempname b
> sca `b' = 5
>
> forval j = 1/100 {
> tempname ed`j'
> }
>
> qui replace `lnf' = 0
> forval i = 1/100 {
> qui gen double `ed`i'' = -ln(uniform())*`b'
> qui replace `lnf'= `lnf' + invlogit( `xb'+`ed`i'')
> if $ML_y1 ==
> 1
> qui replace `lnf'= `lnf' + invlogit(-(`xb'+`ed`i''))
> if $ML_y1 ==
> 0
> }
>
> qui replace `lnf' = ln(`lnf')
> end
>
> use binlfp2.dta, clear
> ml model lf my_ll (lfp = k5 k618 age), technique(nr bhhh dfp bfgs)
> ml maximize
>
>
> initial: log likelihood = 2948.488
> alternative: log likelihood = 2952.4599
> rescale: log likelihood = 2952.4599
> (setting optimization to Newton-Raphson)
> numerical derivatives are approximate
> flat or discontinuous region encountered
> Iteration 0: log likelihood = 2949.0411 (not concave)
> numerical derivatives are approximate
> flat or discontinuous region encountered
> Iteration 1: log likelihood = 2950.7797 (not concave)
> numerical derivatives are approximate
> flat or discontinuous region encountered
> no observations
> r(2000);
>
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