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RE: st: suest equivalent for areg.
From
"Schaffer, Mark E" <[email protected]>
To
<[email protected]>
Subject
RE: st: suest equivalent for areg.
Date
Fri, 20 May 2011 21:32:30 +0100
Sumedha,
Why not partial out the fixed effects by hand and then use -regress-? An easy way to do this is with Ben Jann's -center- command. Then do your estimations on the transformed data.
If you're planning on using -suest-, you'll probably need to use the undocumented -dof- option of -regress- when you do your estimations before invoking -suest-. This is because you will have to tell -regress- that you've lost X degrees of freedom because of the X individual effects. The syntax is easy, e.g.,
regress y x1 x2, dof(25)
tells -regress- that there are 25 degrees of freedom, i.e., the macro e(df_r) will have 25 in it.
HTH,
Mark
> -----Original Message-----
> From: [email protected]
> [mailto:[email protected]] On Behalf Of
> Gupta, Sumedha
> Sent: 20 May 2011 17:34
> To: [email protected]
> Subject: RE: st: suest equivalent for areg.
>
> Hi Jorge,
>
> Thank you for your reply but I am running into exactly the
> problem you anticipated. Suest doesn't allow areg and xtreg
> and running ols with individual dummies is not an option with
> svy as that does not allow vce (cluster id).
>
> Does anybody else have any thoughts on this please?
>
> Many thanks.
> Sumedha.
>
>
> ________________________________________
> From: [email protected]
> [[email protected]] on behalf of Jorge
> Eduardo Pérez Pérez [[email protected]]
> Sent: Wednesday, May 18, 2011 5:52 PM
> To: [email protected]
> Subject: Re: st: suest equivalent for areg.
>
> As -xtreg- will not work with -suest- either, the only
> workaround that comes to mind is to use ols with dummy
> variables. This might not be computationaly feasible if you
> have too many individuals. See the code below. It works with
> the caveat that -suest- requires the models to be estimated
> with iweights and the constants obtained are different from
> the -areg- case, but the coefficients on the independent
> variables are the same.
>
> I don't know if this approach produces correct standard
> errors due to your survey data structure. Notice that suest
> does not allow the use of -cluster- and -svy- at the same
> time. Maybe someone else on the list can provide more
> information on this matter.
>
>
> clear
> set seed 20
> set matsize 1000
> webuse nlswork
> keep if id<500
> xtset idcode
> gen w=uniform()
> bys id: replace w=w[1]
> xi i.occ i.ind i.id
> areg age _Iocc* if south==1 [pweight=w], absorb (id)
> vce(cluster id) areg age _Iind* if south==1 [pweight=w],
> absorb (id) vce(cluster id) qui eststo R1: reg age _Iocc*
> _Iid* [iw=w] if south==1 unab a: _Iocc* est tab, b se
> keep(`a' _cons) qui eststo R2: reg age _Iind* _Iid* [iw=w] if
> south==1 unab b: _Iind* est tab, b se keep (`b' _cons) qui
> suest R1 R2, vce(cluster id) est tab, b se keep(`a' `b'
> R1_mean:_cons R2_mean:_cons)
>
> _______________________
> Jorge Eduardo Pérez Pérez
>
>
>
>
> On Wed, May 18, 2011 at 3:13 PM, Gupta, Sumedha
> <[email protected]> wrote:
> > Dear All,
> >
> > I am trying to estimate a linear fixed effects model with
> survey data. Post estimation I want to use esttab to make my
> tables. I try the following code (sorry for not using the
> standard data code for this):
> >
> > . quietly eststo R1: xi: areg A B if subpop1==1 [pweight=wt],
> > absorb (id) vce(cluster id)
> >
> > . quietly eststo R2: xi: areg A C if subpop1==1
> [pweight=wt], absorb
> > (id) vce(cluster id)
> >
> > . quietly suest R1 R2
> >
> > But then I get the following error message:
> >
> > areg is not supported by suest
> > r(322);
> >
> > Can somebody please advise me on a way around this?
> >
> > Many thanks.
> > Sumedha.
> >
> >
> >
> >
> >
> >
> >
> >
> > *
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> > * http://www.ats.ucla.edu/stat/stata/
> >
>
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