Mark and Bill,
Thank you for your answers - they were quite helpful. The lpop
variable does seem to be problematic. What I still have some problems
with is that excluding it actually decreases the correlation. I have
rechecked the lpop data just to make sure there wasn't some mistake
somewhere, but it seems fine.
Thanks again,
Joana
On 05/04/06, William Gould, Stata <[email protected]> wrote:
> To rehash, Joana Quina <[email protected]> reported,
>
> 1. She has estimated the parameters of a model using -xtreg, fe- and
> that the reported correlation between u_i and X_ij*b is .9249.
>
> 2. She has estimated the parameters of the same same model on
> the same data. She then performs a Hausman test that fails to
> reject random effects.
>
> I agree with Mark Schaffer <[email protected]>, who wrote,
>
> > I wonder if this is being driven by the "omnibus" nature of your Hausman
> > test. In your application, it has 12 degrees of freedom, one for each
> > coefficient. I can imagine that, loosely speaking, if one coefficient is
> > "significantly different" between the 2 specifications, and the other 11 are
> > "very similar", then your Hausman test could fail to reject the null that
> > the specifications are both consistent.
>
> In Joana's case, one coefficient changed a lot. The Hausman test is known
> to lack power in such cases.
>
> -- Bill
> [email protected]
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