RE: st: RE: error message: initial values not feasible

[sun samn <shwshang@hotmail.com>]

Hi, Maarten,
   Thank you very much for your wonderful comments!
   I want to two methods will get the same results; but the claim only can
be proved in certain cases; under other cases, only one method
works-the other one gets the error message.
   I am just wondering whether there is a way to choose an appropriate initial value to make it work?
  

Best,
samn 







----------------------------------------
> Date: Wed, 7 Apr 2010 08:58:28 -0700
> From: maartenbuis@yahoo.co.uk
> Subject: RE: st: RE: error message: initial values not feasible
> To: statalist@hsphsun2.harvard.edu
>
> --- On Wed, 7/4/10, sun samn wrote:
>> It is about my simulation: I want to repeat the GMM for
>> 1000 times and see how the GMM estimates are under different
>> data generation process(DGP). Under certain DGP, the code
>> works fine; while when I change the DGP, I get that error
>> message. I even tried to change the initial value of the
>> parameter, but it did not work either.
>> I guess the method is fine since it works for some DGP;
>> while the data or the initial value might not be good. But I
>> cannot change the data, you know, I need to stick to that
>> setting; so it seems like the only thing I can do is to put
>> an appropriate number, right?
>
> Imagine you are someone who tries to answer your question,
> how would the information you profided help in diagnosing the
> problem? It is obviously still not enough. I will give 2
> guesses at what might be going on, but I will not give much
> details as they may very well not be appropriate to your
> situation.
>
> The fact that your evaluator worked on some datasets does
> not mean it is good: some parameterizations are more
> numerically stable than others. See: William Gould (2006)
> Mata Matters: Precision. The Stata Journal, 6(4):550-660.
> http://www.stata-journal.com/article.html?article=pr0025
>
> Your simulated datasets may just be too extreme: e.g.
> very little variation on some variables or very high
> correlations. Look at the datasets that are causing
> problems and make a decision whether they are worth
> going through much trouble to estimate a model in it.
>
> -- Maarten
>
> --------------------------
> Maarten L. Buis
> Institut fuer Soziologie
> Universitaet Tuebingen
> Wilhelmstrasse 36
> 72074 Tuebingen
> Germany
>
> http://www.maartenbuis.nl
> --------------------------
>
>
>
>
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