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Re: st: fixed effects with robust standard errors


From   "Mark Schaffer" <M.E.Schaffer@hw.ac.uk>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: fixed effects with robust standard errors
Date   Tue, 25 Oct 2005 20:48:20 +0100 (BST)

Vivian,

> I am trying to do what Nick suggested in a posting a few years back (see
> below), i.e. to estimate a fixed effects model with
> heteroskedasticity-robust standard errors by transforming the data to
> deviations from means and then running the regress command, with the
> option robust specified.
>
> However, I believe I need to correct the degrees of freedom for the number
> of dummy variables this procedure implicitly estimates. Is it possible to
> do this within the regress command;

There is an undocumented -dof()- option for -regress- that sets the
residual degrees of freedom to the number provided, but I don't see why
you need to do this.  Why not just use -areg-?

--Mark

> should I (not being an expert
> programmer in the least) try to program it myself; or is there some other
> way to change the degrees of freedom used?
>
> Thanks very much,
> Vivian
>
>
>
>>From 	  "Nick Cox" <n.j.cox@durham.ac.uk>
> To 	  <statalist@hsphsun2.harvard.edu>
> Subject 	  RE: st: robust st. errors and fixed effects
> Date 	  Mon, 21 Oct 2002 15:50:47 +0100
>
>> > is there a possibility to estimate a fixed effect model,
>> controlling for
>> > heteroskedasticity.
>> > the comad robust and xtreg don`t work togetehr.
>> > Is there an other way to control for h.?
>> > thanks for your help
>> > peter
>> >
>> ...
>> As was suggested in a posting to Statalist earlier today,
>> you can get
>> -regress- to estimate a fixed effects model for you, and -regress-
>> will of course generate robust SEs.
>>
>> The posting suggested estimating a "least squares dummy variable"
>> (LSDV) regression, with a dummy for each observational unit
>> (individual, firm, whatever).  This is OK unless you have a lot of
>> different units, in which case you get more dummy variables
>> than you
>> can reasonable handle.  A slightly more laborious but also
>> equivalent
>> method is to transform the data by putting it into mean-deviation
>> form, and then estimating on the transformed data.
>
> Mark mentioned -areg- in passing in his posting
> (abbreviated here): this is just to flag its
> availability given many dummy variables.
>
> Nick
> n.j.cox@durham.ac.uk
>
>
>
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>


Prof. Mark Schaffer
Director, CERT
Department of Economics
School of Management & Languages
Heriot-Watt University, Edinburgh EH14 4AS
tel +44-131-451-3494 / fax +44-131-451-3294
email: m.e.schaffer@hw.ac.uk
web: http://www.sml.hw.ac.uk/ecomes



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