st: negative Hausman Test and how to use Xtoverid

[May Ster <mayfrank@gmail.com>]

Dear all,

Please help on this issues as I wonder whether there is such a test to
conclude that the Hausman Taylor is more consistent than the fixed
effects estimator (which lacks lots of efficiency in this case).

I’m currently working on Panel Data . In my data, panel var = 90
units, time var = 38 years.
In the model, It is assume to be a linear regression containing
individual effects ;

I use Xtreg lnA lnB lnC Dummy1 Dummy2 Dummy3

I then have checked for the appropriateness of the individual effects
model to be use. [The Hausman test proves fixed effects model is
preferred over the random effects model]

Then, I took the benefit of the Hausman-Taylor estimation as according
to the literatures the model should contain correlations between some
of explanatory variables and individual effects. As well, using fixed
effects model won't be able to observe time-invariant variables.

My question is, is it necessary to perform another Hausman Test to
state again that the Hausman-Taylor is a consistent estimator?

However, I actually conducted the Hausman Test and found the test
showing negative value. I then consulted Statalist so far on this
issue. A lot of them say this case is common and I should do as STATA
suggested; using ‘Suest’. But, ‘Suest’ can’t be applied with
‘Xtreg’,and then I became very doubtful.

Another way is to use the command ‘XTOVERID’ after ‘Xthtaylor’ can be
the way to obtain positive value of Sargan-Hansen Statistic. But if I
understand it correctly, ‘XTOVERID’ is an over-identifying test to
sort of signal that instruments I have chosen are appropriate. I still
do not see how ‘XTOVERID’ can be used to compare whether the
Hausman-Taylor is more consistent than the fixed-effects in this case.
I am certain that some use ‘XTOVERID’ to decide the fixed effect
versus the random effects models. But, how is it done about comparing
the Hausman-Taylor verus the fixed effects model?


Thank you very much in advance,
Best Regards,

Orachat


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