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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 13:37:59 -0500 |

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 <M.E.Schaffer@hw.ac.uk> wrote: > Richard, Austin, and (hopefully) Jeff, > > > -----Original Message----- > > From: owner-statalist@hsphsun2.harvard.edu > > [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of > > Austin Nichols > > Sent: 13 September 2007 09:13 > > To: statalist@hsphsun2.harvard.edu > > Subject: Re: st: suest with large number of fixed effects > > > > 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? > > 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 <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. > > * > > * 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: > * 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: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**Re: st: suest with large number of fixed effects***From:*"Austin Nichols" <austinnichols@gmail.com>

**RE: st: suest with large number of fixed effects***From:*"Schaffer, Mark E" <M.E.Schaffer@hw.ac.uk>

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