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From |
"Martin Weiss" <martin.weiss1@gmx.de> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
AW: st: AW: how to get slopes by clusters in a linear regression |

Date |
Tue, 25 Aug 2009 21:33:03 +0200 |

<> " sortby c: reg m2 t2" Probably -bysort-? HTH Martin -----Ursprüngliche Nachricht----- Von: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] Im Auftrag von Austin Nichols Gesendet: Dienstag, 25. August 2009 20:37 An: statalist@hsphsun2.harvard.edu Betreff: Re: st: AW: how to get slopes by clusters in a linear regression László Sándor <sandorl@gmail.com>: Your code does not run as written, and the approach you outline does not do what you want (unless I am misunderstanding your problem): webuse grunfeld, clear set seed 1 ren company c keep if c<5 qui ta c, g(d_) g byte t=uniform()>.5 forv i=1/4 { g byte td_`i'=d_`i'*t } drop d_1 reg mvalue kstock d_* td_* g good=. forv i=1/4 { replace good=_b[td_`i'] if c==`i' } g bad=. qui reg mvalue kstock predict m2, res qui reg t kstock predict t2, res forv i=1/4 { qui reg m2 t2 if c==`i' replace bad=_b[t2] if c==`i' } tabdisp c, c(good bad) Again: Make sure you are getting the right answer in a small subsample before you go too far... 2009/8/25 László Sándor <sandorl@gmail.com>: > Austin, > thanks for the code. I meant the latter approach, or even simpler, as > you don't need interactions if you will estimate it cluster by > cluster. > > In your code (without collecting the coefficients): > > qui reg mvalue kstock > predict m2, res > qui reg t kstock > predict t2, res > sortby c: reg m2 t2 > > The last can be made more useful with statsby, as Martin suggested. > > Thanks, > > Laszlo > > > On Tue, Aug 25, 2009 at 1:31 PM, Austin Nichols<austinnichols@gmail.com> wrote: >> László Sándor <sandorl@gmail.com>: >> Using the FWL thm is the fastest way to go, probably, but I am not >> clear on what you are actually doing. Is it something like one of the >> approaches below? Or are you trying to get the individual-specific >> coefs on the treatment dummy (harder, but can be done in Mata without >> making any really big matrices)? >> >> webuse grunfeld, clear >> set seed 1 >> ren company c >> keep if c<5 >> qui ta c, g(d_) >> g byte t=uniform()>.5 >> forv i=1/4 { >> g byte td_`i'=d_`i'*t >> } >> drop d_1 >> reg mvalue kstock d_* td_* >> est sto reg1 >> egen double m1=mean(mvalue), by(c t) >> g double mres=mvalue-m1 >> egen double k1=mean(kstock), by(c t) >> g double kres=kstock-k1 >> areg mres kres, a(c) >> est sto reg2 >> egen c2=group(t c) >> areg mvalue kstock, a(c2) >> est sto reg3 >> est table reg? >> >> Or are you doing something like: >> >> qui reg mvalue td_2 td_3 td_4 d_* kstock >> predict m2, res >> qui reg td_1 td_2 td_3 td_4 d_* kstock >> predict t2, res >> reg m2 t2 >> >> which still needs big matrices as the problem gets big? Make sure you >> are getting the right answer in a small subsample before you go too >> far... >> >> 2009/8/25 László Sándor <sandorl@gmail.com>: >>> Thank you, Martin! >>> >>> I don't see how this could be used to estimate the model in a single >>> command -- statsby still seem to break down the regression by >>> clusters, without the intercluster restrictions on the >>> controls/covariates. >>> >>> However, this led me to try to apply the Frisch-Waugh-Lowell theorem: >>> I estimate the univariate regression of outcome on treatment by each >>> cluster, I must only use the residuals for both after regressing them >>> on the set of controls. >>> >>> If there is no other way, this seems to be doable. >>> >>> Thanks again! >>> >>> Laszlo >>> >>> On Tue, Aug 25, 2009 at 11:23 AM, Martin Weiss<martin.weiss1@gmx.de> wrote: >>>> >>>> <> >>>> >>>> >>>> ****** >>>> h statsby >>>> ****** >>>> >>>> >>>> HTH >>>> Martin >>>> >>>> >>>> -----Ursprüngliche Nachricht----- >>>> Von: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] Im Auftrag von László Sándor >>>> Gesendet: Dienstag, 25. August 2009 17:20 >>>> An: statalist@hsphsun2.harvard.edu >>>> Betreff: st: how to get slopes by clusters in a linear regression >>>> >>>> Dear Fellow Statalisters, >>>> >>>> I want to extend a fixed-effects-type model to allow for different >>>> coefficients on a variable (actually a treatment dummy) by each >>>> cluster I have. The richness of my data would allow for that. However, >>>> I did not find a way to do it in Stata that would report (and collect) >>>> the coefficients themselves. -xtmixed- doesn't seem to do so. I would >>>> like to restrict the coefficients on controls to be equal across >>>> clusters, so estimation by cluster is not a solution either. >>>> >>>> If there were a way that could collect the slopes to a single new >>>> variable (with the same value for observations in the same cluster, >>>> naturally), that would be the best. It would be great if I did not >>>> need to introduce all the 1438 cluster-indicator variables and >>>> interactions myself, and collect the coefficients. >>>> >>>> Thank you for any guidance in advance! >>>> >>>> Laszlo * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: how to get slopes by clusters in a linear regression***From:*László Sándor <sandorl@gmail.com>

**Re: st: AW: how to get slopes by clusters in a linear regression***From:*László Sándor <sandorl@gmail.com>

**Re: st: AW: how to get slopes by clusters in a linear regression***From:*Austin Nichols <austinnichols@gmail.com>

**Re: st: AW: how to get slopes by clusters in a linear regression***From:*László Sándor <sandorl@gmail.com>

**Re: st: AW: how to get slopes by clusters in a linear regression***From:*Austin Nichols <austinnichols@gmail.com>

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