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# Re: st: Test for heteroscedasticity in panel data in STATA

 From Madalina Constantin To statalist@hsphsun2.harvard.edu Subject Re: st: Test for heteroscedasticity in panel data in STATA Date Wed, 26 Jun 2013 14:40:29 -0700 (PDT)

```Thanks for the link.
I did a lrtest and this is what I got:

xtgls dep indep , panels(heterosk) igls
estimates store hetero
xtgls dep indep , igls
estimates store homosk
local df = e(N_g) - 1
lrtest hetero homosk , df(104)

Likelihood-ratio test			LR chi2(104)=	-6718.22
(Assumption: hetero nested in homosk)	Prob > chi2 =	1.0000

Can I conclude from this that the result is not significant, thus  there is no problem of heteroscedasticity?

For plotting the residuals I only know folowing command:
rvfplot, yline(0)
which again doesn`t work for panel data.
Is there another possibility?

--- On Wed, 6/26/13, Gordon Hughes <G.A.Hughes@ed.ac.uk> wrote:

> From: Gordon Hughes <G.A.Hughes@ed.ac.uk>
> Subject: Re: st: Test for heteroscedasticity in panel data in STATA
> To: statalist@hsphsun2.harvard.edu
> Date: Wednesday, June 26, 2013, 11:56 PM
> You should take a step back and ask
> yourself how heteroskedasticity might manifest itself in
> your panel.  Since there are various sources of
> potential heteroskedasticity, you may need to adopt
> different model specifications to test different ones.
>
> The classic form is panel-level heteroskedasticity but with
> 6 years for each of 104 companies you have not got enough
> observations to test this properly. There is an FAQ at:
>
> <http://www.stata.com/support/faqs/statistics/panel-level-heteroskedasticity-and-autocorrelation/>http://www.stata.com/support/faqs/statistics/panel-level-heteroskedasticity-and-autocorrelation/
>
>
> which is extended in a presentation by Gustavo Sanchez of
> StataCorp which you will find at:
>
> <http://cecip.upaep.mx/investigacion/CIIE/assets/docs/doc00026.pdf>http://cecip.upaep.mx/investigacion/CIIE/assets/docs/doc00026.pdf
> .
>
> In general, you would be best advised to plot or otherwise
> transform or reformulate your models to eliminate any
> obvious heteroskedasticity.
>
>
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```