RE: st: RE: simulated maximum likelihood estimation

["Jun Xu" <mystata@hotmail.com>]

Sorry for that mistake. The following is a revised version. Still encounter 
difficulty calculating numerical derivatives. I don't think it's data 
problem. I tried to look into mvprob_ll.ado by Dr. Cappellari and Dr. 
Jenkins, and I am stuck there. No one is responsible for solving my problem, 
except myself; however, I do need some even slight hint to get me through.

********************************
set trace off
set more off
cap program drop my_ll
program my_ll
   version 8.2
   args lnf xb
   tempname b sp0
   sca `b' = 5

   forval i = 1/100 {
       tempname ed`i' like`j' sp`i'
       gen double `sp`i'' = 0
   }

   gen double `sp0' = 1

   qui replace `lnf' = 0
   forval i = 1/100 {
       loc j = `i' - 1
       qui gen double `ed`i'' = -ln(uniform())*`b'
       qui replace `sp`i''= `sp`j''*(invlogit( `xb'+`ed`i''))      if 
$ML_y1 == 1
       qui replace `sp`i''= `sp`j''*(invlogit(-(`xb'+`ed`i'')))    if 
$ML_y1 == 0
   }

   qui replace `lnf' = ln(`sp100')
end

use binlfp2.dta, clear
ml model lf my_ll (lfp = k5 k618 age), technique(nr bhhh dfp bfgs)
ml search
ml maximize

**************************************************

Jun Xu
Ph.D. Candidate
Department of Sociology
Indiana University at Bloomington
http://mypage.iu.edu/~junxu/home





> From: "Nick Cox" <n.j.cox@durham.ac.uk>
> Reply-To: statalist@hsphsun2.harvard.edu
> To: <statalist@hsphsun2.harvard.edu>
> Subject: st: RE: simulated maximum likelihood estimation
> Date: Mon, 9 May 2005 01:11:33 +0100
>
> 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
> n.j.cox@durham.ac.uk
>
> 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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