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# Re: st: Robust version of the Hausman test... contradicting results

 From Sami Alameen To statalist@hsphsun2.harvard.edu Subject Re: st: Robust version of the Hausman test... contradicting results Date Tue, 12 Jul 2011 18:27:07 +0300

Mari,

in: reg  redy  redx1 redx2 redx3 redx4  mdx1 mdx2 mdx3 mdx4, vce(cluster id)

You are testing if Cross-section (id) significant versus common
intercept, not versus RE.
Also instead of the mdx? that Stata drops you should test if
_cons=mdx1=mdx2=mdx3 and so on, because Stata uses a common _cons and
make mdx# the difference from that intercept (_cons refers to the
dropped category)

That's said doesn't mean the result will always be consistent among
the two test, but you were testing different thing than you intended.

To be in the right direction compare:
xtreg y x ..., re
est store re
xtreg y x..., fe
hausman . re

to test FE against RE
then compare to the Wooldridge's robust stat

Good luck

On Tue, Jul 12, 2011 at 3:48 PM, Mari Mari <mari4i@yahoo.com> wrote:
> Dear Statalist,
>
> I am deciding whether to use a FE or RE model, based on the results of a robust
> version of the Hausman test. I am using Stata 11. Here is what I did for my
> balanced panel.
>
> * Robust version of the Hausman test (Wooldridge 2002)
> quietly xtreg y x1 x2 x3 x4, re
> by id: gen T=_N
> gen theta=1-sqrt(e(sigma_e)^2/(e(sigma_e)^2+ T*e(sigma_u)^2))
> foreach x in y x1 x2 x3 x4 {
>    by id: egen mean`x' = mean(`x')
>        generate md`x' = `x' - mean`x'
>        generate red`x' = `x' - theta*mean`x'
> }
> quietly reg  redy  redx1 redx2 redx3 redx4  mdx1 mdx2 mdx3 mdx4, vce(cluster id)
> test mdx1 mdx2 mdx3 mdx4
>  ( 1)  mdx1 = 0
>  ( 2)  mdx2 =  0
>  ( 3)  mdx3 = 0
>  ( 4)  mdx4 = 0
>       F(  4,    25) =   11.72
>            Prob > F =    0.0000
>
> This suggests I should use FE
>
> _________________________________________________________________________
>
> * Robust version of the Hausman test (Schaffer and Stillman, 2006)
> quietly xtreg y x1 x2 x3 x4, re cluster(id)
> xtoverid
> Sargan-Hansen statistic   5.885  Chi-sq(4)    P-value = 0.2079
>
> This suggests I should use RE
>
>
> Given the tests are providing opposite results, do you have any suggestion on
> how to proceed?
>
> Thanks!
>
> Maria
>
>
> *
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>

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