Bookmark and Share

Notice: On April 23, 2014, Statalist moved from an email list to a forum, based at

[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

Re: st: Testing for panel-level heteroskedasticity with xtgls

From   Clive Nicholas <[email protected]>
To   [email protected]
Subject   Re: st: Testing for panel-level heteroskedasticity with xtgls
Date   Tue, 1 Feb 2011 01:30:48 +0000

[email protected] wrote:

> I am a bit confused from the result I got for a LR test. I want to test about
> heteroscedasticity across panels as suggested by the Stata team in their FAQ
> section.
> In particular, it is suggested to calculate iterated GLS with only
> heteroskedasticity first and save the likelihood. Then to fit the model but
> without the heteroscedasticity assumption (though it is not specified if this
> second model shall be iterated or it shall be left without this option).
> Below is the result I got. i would like also to ask if  my p-value of the test
> now indicates that I have heteroscedasticity or actually not? I am confused
> because they provide no explanation. Moreover, is it a bad sign to get no output
> of estimates for ll(null)?
> . lrtest hetero . , df(`df') stats
> Likelihood-ratio test                                  LR chi2(16) =    493.43
> (Assumption: . nested in hetero)                       Prob > chi2 =    0.0000
> -----------------------------------------------------------------------------
>   Model |    Obs    ll(null)   ll(model)     df          AIC         BIC
> -------------+---------------------------------------------------------------
>           . |    367           .   -29.55142     29     117.1028    230.3583
>   hetero |    367           .    217.1648     46    -342.3296   -162.6829
> -----------------------------------------------------------------------------
>               Note:  N=Obs used in calculating BIC; see [R] BIC note

Going simply by the significance of your LR chi-square statistic in
your test, this would appear to support the hypothesis that your IGLS
model does have panel-level heteroscedascity.

Since this is case, maybe you should switch to fitting an OLS model
with panel-corrected standard errors (-xtpcse-) to deal with this

Clive Nicholas

[Please DO NOT mail me personally here, but at
<[email protected]>. Please respond to contributions I make in
a list thread here. Thanks!]

"My colleagues in the social sciences talk a great deal about
methodology. I prefer to call it style." -- Freeman J. Dyson.

*   For searches and help try:

© Copyright 1996–2018 StataCorp LLC   |   Terms of use   |   Privacy   |   Contact us   |   Site index