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RE: st: a problem on convergence of mle


From   Jian Zhang <jzh@ucdavis.edu>
To   statalist@hsphsun2.harvard.edu
Subject   RE: st: a problem on convergence of mle
Date   Thu, 19 Oct 2006 11:53:00 -0700 (PDT)

Thanks, Maarten!

Regards,
Jian

On Wed, 18 Oct 2006, Maarten Buis wrote:

> Jian Zhang:
> It seems that you have faithfully copied equation 11 to `lnf', however I could
> not find where `g' came from.
>
> One simplification that could make it easier for Stata to find the maximum is
> possible: -1/2*ln((`sigma')^2) = -1/2*2*ln(`sigma') = -ln(`sigma')
>
> If you are confident the likelihood equation is correct, than using the
> difficult option when calling -ml maximize- can sometimes help. See
> -help maximize- for more on this option.
>
> HTH,
> Maarten
>
> -----------------------------------------
> Maarten L. Buis
> Department of Social Research Methodology
> Vrije Universiteit Amsterdam
> Boelelaan 1081
> 1081 HV Amsterdam
> The Netherlands
>
> visiting adress:
> Buitenveldertselaan 3 (Metropolitan), room Z434
>
> +31 20 5986715
>
> http://home.fsw.vu.nl/m.buis/
> -----------------------------------------
>
> --- Jian Zhang wrote:
> My loglikelihood function comes from equation 11 of the paper by Carroll,
> Dynan and Krane in Review of Economics and Statistics 85(3) 2003,
> Here is the program I wrote for the loglikelihood function for equation 11 in their paper.
>
> capture program drop transform1
> program transform1
>         version 9.2
>         args lnf xb sigma theta
>         tempvar g
>         quietly {
>                 gen double `g'=(ln(`theta'*$ML_y1+sqrt((`theta')^2*($ML_y1)^2+1)))/`theta'
>                 replace `lnf'=-1/2*ln((`sigma')^2)-1/2*1/((`sigma')^2)*(`g'-`xb')^2 ///
>                 -1/2*ln(1+(`theta')^2*($ML_y1)^2)
>         }
> end
> ml model lf transform1 (dependent variable=independent variable ....) /sigma /theta, robust
> cluster(groupvar)
> ml maximize
>
> When I used the whole sample, the loglikelihood didn't converge.  But when I used a subsample,
> it in fact converged.  Thus it seems to me that the log likelihood function is fine.
>
>
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