Thanks for the post. I agree with what is written and I am similarly confused.
I read Jeff's reply below, and I am not sure I really understand the difficulty.
On 9/13/07, Schaffer, Mark E <[email protected]> wrote:
> Richard, Austin, and (hopefully) Jeff,
>
> > -----Original Message-----
> > From: [email protected]
> > [mailto:[email protected]] On Behalf Of
> > Austin Nichols
> > Sent: 13 September 2007 09:13
> > To: [email protected]
> > Subject: Re: st: suest with large number of fixed effects
> >
> > Richard Boylan <[email protected]>:
> > Interesting. I used a not-up-to-date Stata 9.2 (20 Jan 2006)
> > and the example ran fine (danger of not being able to use
> > -update qu- with a nonstandard winsock.dll, and being
> > forgetful, I suppose), but produced absurdly low p-values
> > (i.e. absurdly small SEs relative to the individual
> > regressions, esp with -cl(id)- corrections). After a manual
> > update (to 20 July 2007), I got the error message you report,
> > but I don't see the relevant change in -help whatsnew-
> > anywhere (a post to Statalist by Jeff Pitblado on June 27,
> > 2006, indicates it probably happened in the 6 July 06 update
> > which was also the MP release).
> >
> > Mark Schaffer gives a way forward by manually demeaning, but
> > I suppose any concerns Stata has about applying -suest- to
> > -areg- would apply to any such -reg c_y c_x- procedure?
>
> I had forgotten about Jeff's post. Here is the key extract:
>
> "We've recently discovered that -suest- yields incorrect results when
> used
> after -areg-. This is not something that can easily be fixed given that
> the
> meat of the sandwich estimator of variance (Robust/Huber/White VCE)
> cannot be properly computed due to the fact that the coefficient
> estimates for the
> absorbed categories in -areg- are not present in -e(b)- (and the
> corresponding indicators are not present in the dataset)."
>
> and here's a link to the full post:
>
> http://www.stata.com/statalist/archive/2006-06/msg00874.html
>
> What puzzles me about Jeff's statement is that absorbing
> (partialling-out, demeaning, removing through first-differencing) the
> fixed effects is no obstacle to using the
> sandwich/robust/Huber/White/and-don't-forget-Eicker VCE with other
> estimators.
>
> For example, demeaning and using -regress- with -cluster- generates
> exactly the same results as -xtreg,fe cluster()- and indeed -areg-, as
> this example shows (again using Ben Jann's -center- command):
>
> sort id
> by id: center ys k n, casewise
> reg c_ys c_k c_n, cluster(id)
> xtreg ys k n, i(id) cluster(id) fe
> areg ys k n, absorb(id) cluster(id)
>
> If it's legitimate to use the robust VCE with transformed individual
> equations, shouldn't it also be legitimate to use -suest- to combine the
> equations? The "within-equation" parts of the VCE reported by -suest-
> will be the same (though maybe with a different and asymptotically
> uninteresting dof adjustment) as that obtained by estimating the
> separate equations on their own. -suest- is just adding the
> "cross-equation" part of the VCE.
>
> I'd be very interested in hearing more about this from Jeff or anyone
> else.
>
> Cheers,
> Mark
>
> > Might you perhaps compare the manual approach using demeaned
> > data to one using the two-way clustering approach of Cameron
> > Gelbach and Miller (paper at
> > http://nber.org/papers/t0327 and code at
> > http://glue.umd.edu/~gelbach/ado/cgmreg.ado) on a stacked
> > dataset? In any case, I think there is good reason to prefer
> > (to the -suest- approach you'd like to use, clustering on obs
> > across panels, and on panel across obs) the SEs you get from
> > the individual regressions you
> > specified:
> >
> > xtreg y1 x1, i(id) fe cl(id)
> > xtreg y2 x2, i(id) fe cl(id)
> > xtreg y3 x3, i(id) fe cl(id)
> >
> > is it true you don't have any common regressors across equations?
> >
> > On 9/12/07, Richard Boylan <[email protected]> wrote:
> > > Thanks, but I should have mentioned that a previous post on the
> > > statalist discusses how areg cannot be not be used with
> > suest because
> > > those estimates are incorrect. So, if I have to assume that you are
> > > using an older version of STATA (7 or older) that allows you to use
> > > suest with areg.
> > >
> > > In the newer version of one tries to do that one obtains
> > >
> > > areg is not supported by suest
> > >
> > >
> > > On 9/12/07, Austin Nichols <[email protected]> wrote:
> > > > Richard Boylan--
> > > > Indeed -areg- will mechanically give you an answer when combined
> > > > with
> > > > -suest- but you should be aware that the cluster-robust estimator
> > > > can give downward-biased estimates of the true standard
> > deviation of
> > > > your estimates, so a smaller p-value after -suest- may be
> > suspect.
> > > > Also, just to be clear, -suest- will not give you more precisely
> > > > estimated coefficients since it will not change your
> > estimated coefficients.
> > > > Under some circumstances it will give you better estimates of the
> > > > standard errors, and those circumstances include having a large
> > > > number of clusters and observations. How large? That depends...
> > > >
> > > > webuse abdata
> > > > areg ys n w k, a(id)
> > > > est sto ys
> > > > areg wage n w k, a(id)
> > > > est sto wage
> > > > suest wage ys, cluster(id)
> > > >
> > > > On 9/12/07, David Jacobs <[email protected]> wrote:
> > > > > The command "areg" is designed for this purpose, but
> > I'm not 100%
> > > > > sure that it has a score option or that it's matrix
> > isn't equally large.
> > > > >
> > > > > Dave Jacobs
> > > > >
> > > > > At 11:11 AM 9/12/2007, you wrote:
> > > > > >I would like to estimate several regressions separately, but
> > > > > >using suest to obtain more precisely estimate coefficients
> > > > > >
> > > > > >So, what I would like to do is:
> > > > > >
> > > > > >xtreg y1 x1, i(id) fe
> > > > > >est store eq1
> > > > > >xtreg y2 x2, i(id) fe
> > > > > >est store eq2
> > > > > >xtreg y3 x3, i(id) fe
> > > > > >est store eq3
> > > > > >suest eq1 eq2 eq3, cluster(id)
> > > > > >
> > > > > >Given that xtreg does not have a score option, it is
> > discussed in
> > > > > >previous postings that one needs to estimate the model using a
> > > > > >linear regression with dummy variables.
> > > > > >
> > > > > >The problem I have is that I have 1000 fixed effects
> > and thus the
> > > > > >matrix computed in suest is going to be way too large.
> > *
> > * For searches and help try:
> > * http://www.stata.com/support/faqs/res/findit.html
> > * http://www.stata.com/support/statalist/faq
> > * http://www.ats.ucla.edu/stat/stata/
> >
>
> *
> * For searches and help try:
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>
*
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