# Re: st: AW: how to get slopes by clusters in a linear regression

 From László Sándor To statalist@hsphsun2.harvard.edu Subject Re: st: AW: how to get slopes by clusters in a linear regression Date Tue, 25 Aug 2009 13:51:25 -0400

```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
>
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