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RE: st: LR-tests and Xtnbreg


From   Jon Heron <jon.heron@bristol.ac.uk>
To   statalist <statalist@hsphsun2.harvard.edu>
Subject   RE: st: LR-tests and Xtnbreg
Date   Tue, 3 Jun 2003 14:16:30 +0100



 Me again,


 in attempt to gain a better understanding, I am trying to
 verify the relationship between alpha in the mean-dispersion
 nbreg model and delta in the constant-dispersion nbreg model.

 This works fine in a pair of models containing just a constant
 however breaks down when I add a 2 level factor.

 How is the single value of delta calculated from the two delta_i
 from the two groups?  It doesn't appear to be the arithmetic
 nor geometric mean.


 thanks in advance



 Jon

>
> > If so, why, when I compare the likelihoods of an xtnbreg model with it's
> > equivalent nbreg model does (2 x) the difference not agree with the
> > chi-square value quoted in this test?  The discrepancy is
> slight, but I do
> > obtain an exact agreement when comparing the likelihoods of poisson and
> > xtpois in the same way.
>
> Note that there are two parameterizations of the negative binomial that
> are available under -nbreg-, the mean dispersion (default) and
> the constant
> dispersion (option -dispersion(constant)-).
>
> The model associated with -xtnbreg- is a generalization of the constant
> dispersion parameterization of the negative binomial.  Thus, if
> you are going
> to calculate likelihood ratio tests manually, you need to specify
> -dispersion(constant)- with -nbreg-.
>
> > Furthermore, since these 4 models appear to be nested, why
> can't I use the
> > 'lrtest' command to assess their relative worth?
>
> -lrtest- will complain if your models, even though nested, are
> fitted using
> different estimation commands.  A little too cautious perhaps, yet we feel
> this is a necessary safeguard against calculating likelihood ratios for
> non-nested models.
>
> When you are sure that the models are nested, you can turn off
> this safeguard
> by specifying option -force- to -lrtest-.
>
> --Bobby
> rgutierrez@stata.com
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>

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