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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 13:34:49 -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. > * > * 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**:**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>

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

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