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RE: st: Negative LR test statistic ?


From   "Nick Cox" <n.j.cox@durham.ac.uk>
To   <statalist@hsphsun2.harvard.edu>
Subject   RE: st: Negative LR test statistic ?
Date   Tue, 22 Dec 2009 10:52:42 -0000

The sample size remains what it was. Maarten's informal but to me convincing argument is in terms of how much information you have per equation, and the answer is "Not much as you appear to think". 

Nick 
n.j.cox@durham.ac.uk 

Ekrem Kalkan

Thank you very much Maarten,
I did not know that the sample size reduces to 860/14 = 61.4  for each
equation in  system estimation methods.


2009/12/22 Maarten buis <maartenbuis@yahoo.co.uk>:
> --- On Tue, 22/12/09, Ekrem Kalkan wrote:
>> in my case  every equation has 860 number of
>> observartion.
>
> I interpret that as meaning that your dataset contains
> 860 observations, and you are estimating a model with
> 14 equations, is that correct? In that case you have
> only 860/14= 61.4 observations per equation, which was
> my point.
>
>> Still, I think there should be a different
>> reason rather than degrees of freedom.
>
> I think degrees of freedom is the reason why you get
> negative likelihood ratio statistics. Remember that
> the reasoning behind the likelihood ratio test is
> asymptotic, so strictly speaking it is only valid in
> case of a infinitly large sample. What sample size is
> close enough to infinity depends on the kind of
> information you are trying to extract from: the more
> complex the information you are trying to extract the
> larger the sample size needs to be.

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