I use Stata 9.2
You can just add the robust option after the comma: xtreg y x, robut.
On Statalist, we have some "rules" (see the FAQ): Stata commands should be cited between dashes - -
You can download -xtscc- by typing ssc install xtscc. Please note that -xtscc- let you have far more than robust standard errors (but not random effects).
Nicola
At 02.33 25/09/2007 -0400, Jasminka wrote:
>Quoting [email protected]:
>
>hi Nicola,
>
>it seems that your answer is usefull for me, too. namely, I asked about
>how to estimate random effects with robust errors. the question still
>stays: which Stata version are you using? I can not find command xtscc
>in my 9.2 version. and what about dashes? could you, please, clarify?
>
>thank you so much.
>Jasminka
>
>> -xtscc- (remember the dashes!) is useful for both cross-sectional
>> dependence and heteroskedasticity, while fixed effects (it's one of
>> the two options) allows for endogeneity
>> Nicola
>> At 02.33 21/09/2007 -0400, "Ivan Etzo" wrote:
>>> Dear All,
>>>
>>> my panel (N=3D2660 ; T=3D7) presents either heteroskedasticity =
>>> (lrtest and xttest3) endogeneity and cross-sectional dependence
>>> (xtc= sd test). Moreover there is also serial correlation (xtserial
>>> test).
>>> Thank to statalist I found useful commands to correct my analysis
>>> bu= t I haven't found a way to cope with all of them together yet.&=
>>> nbsp;The command xtscc for ex. is useful for the cross-sectional
>>> dependen= ce (nd maybe also heteroskedasticity..?) but not in the
>>> case of endogenou= s covariates. For the endogeneity problem, I
>>> tried different commands lik= e ivreg2 and xthtaylor, the last one
>>> give nice results in terms of expect= ed coeff signs and their
>>> significance, but they don't provide correct SE = for cross-sect
>>> dependence. Xtgls seems good for heteroskedasticity (= and serial
>>> correlation) but not for endogeneity.
>>> I can say that I'm new with empirical analysis and panel data so
>>> eve= ry suggestion or comment would be pretty appreciated.
>>>
>>> thanks to all
>>>
>>> Ivan
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