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Re: st: suest with large number of fixed effects


From   "Richard Boylan" <rtboylan@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: suest with large number of fixed effects
Date   Thu, 13 Sep 2007 13:34:49 -0500

These are different observations. So, the only thing that the
different regressions have in common are the same fixed effects.

Richard

On 9/13/07, Austin Nichols <austinnichols@gmail.com> wrote:
> Richard Boylan <rtboylan@gmail.com>:
> 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?  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 <rtboylan@gmail.com> 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 <austinnichols@gmail.com> 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 <jacobs.184@sociology.osu.edu> 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.
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