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From |
"Austin Nichols" <austinnichols@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: suest with large number of fixed effects |

Date |
Thu, 13 Sep 2007 04:12:45 -0400 |

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. * * 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/

**Follow-Ups**:**Re: st: suest with large number of fixed effects***From:*"Richard Boylan" <rtboylan@gmail.com>

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

**References**:**st: suest with large number of fixed effects***From:*"Richard Boylan" <rtboylan@gmail.com>

**Re: st: suest with large number of fixed effects***From:*David Jacobs <jacobs.184@sociology.osu.edu>

**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:*"Richard Boylan" <rtboylan@gmail.com>

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