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Re: st: xtivreg2 gmm2 : robust vs cluster(id) vs bw(#) dkraay(#)


From   Austin Nichols <austinnichols@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: xtivreg2 gmm2 : robust vs cluster(id) vs bw(#) dkraay(#)
Date   Fri, 16 Mar 2012 11:13:07 -0400

Paulo Regis <pauloregis.ar@googlemail.com>:
http://www.stata-journal.com/sjpdf.html?articlenum=st0030_3
explains how variance estimates can be used in constructing more
efficient estimates in GMM.

On Fri, Mar 16, 2012 at 5:55 AM, Paulo Regis
<pauloregis.ar@googlemail.com> wrote:
> Dear colleagues,
>
> I have a question regarding the command -xtivreg2. Usually, when
> working with  robust standard errors,this affects the standard error
> of the coefficients but not the coefficients themselves. This is true
> in -regress or -xtreg, for example. This is also true in 2SLS (see
> example 1 below using xtivreg2 and comparing robust to heterosk and
> Clustered SE).
>
> However, I got into trouble when applying the GMM version of
> -xtivreg2. In example 2 below, the use of alternative robust standard
> errors actually have an impact on the coefficients. In fact, when
> using the different roust standard  errors white 1980 (robust),
> Clusters (clusters(id)) Newey and West 1987 (bt(#)), Driscoll and
> Kraay 1998 (dkraay(#)) available with "xtivreg2   , gmm2", you will
> always get not only different standard errors but also different
> coefficients.
>
> I woudl appreciate if anyone of this list could give me a hint on why
> this is the case for GMM. Also, I would be more tahn appy if you can
> point at some references to look at this a bit in more detail. For
> example, I have looked at the documentation related to the command
> -ivreg2 but I could not find any explanation of this.
>
> Bests,
>
> Paulo
>
>
> use http://fmwww.bc.edu/ec-p/data/macro/abdata.dta
> tsset id year
>
> *Example 1: 2SLS
> xtivreg2 ys k (n=l2.n l3.n), fe robust
> xtivreg2 ys k (n=l2.n l3.n), fe cluster(id)
>
>
> *Example 2: GMM
> xtivreg2 ys k (n=l2.n l3.n), fe gmm2 robust
> xtivreg2 ys k (n=l2.n l3.n), fe gmm2 cluster(id)

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