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RE: st: GMM speed


From   "Li, Wei" <LiW@darden.virginia.edu>
To   "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>
Subject   RE: st: GMM speed
Date   Sun, 22 May 2011 14:10:17 -0400

Many thanks, Brian. I will work on linearizing the specification. I wonder if a future version of stata might include an option to use more memory so as to improve speed.

Best, 

Wei



> -----Original Message-----
> From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-
> statalist@hsphsun2.harvard.edu] On Behalf Of Brian P. Poi
> Sent: Saturday, May 21, 2011 10:10 PM
> To: statalist@hsphsun2.harvard.edu
> Subject: Re: st: GMM speed
> 
> On 05/21/2011 09:17 PM, Li, Wei wrote:
> > Dear Statalist Members,
> >
> > I tried to run the following simple nonlinear GMM specification with over 2
> million observations. It took more than 10 hours to do iteration 0. I had to
> cancel the procedure and rerun the estimation using a 1% sample of the data
> to test the specification. I guess I could get a workstation with more than one
> processors and stata/MP... But that would be expensive...
> >
> > I also tried to include the derivatives in the stata statement. Doing that
> increased speed quite a bit using the 1% sample. It is still taking a very long
> time (it is almost 16 hours and I am still waiting).
> >
> > Any suggestions?
> >
> >
> > gmm
> > (y-{a=0.4}*L.y-ln({b=0.1}*x1+{c=4}*x2+x3)+{a}*ln({a}*L.x1+{b}*L.x2+L.x
> > 3)-{c=0}),xtinstruments(y x1 x2 x3, lags(2/4)) instruments(L.x1)
> > winitial(xt D) wmat(hac nw 2) vce(unadjusted)
> >
> 
> Is the nonlinear specification essential?  Could you instead fit a linear model
> and interpret it as a first-order series expansion of your nonlinear model?  If
> that is the case, then you could use -xtabond-, -xtdpd-, -xtdpdsys-, or David
> Roodman's -xtabond2- (available on SSC and described in volume 9, issue 1 of
> the Stata Journal)?
> 
> Those estimators can build up the entire instrument matrix all at once, and
> since they are linear estimators, are much quicker.
> 
> -gmm- provides much more flexibility, but that comes at a cost.  -gmm- builds
> up the panel-style instrument matrix for each panel individually when
> computing the GMM criterion function, and it does not save those matrices
> from one function evaluation to the next because with large datasets with
> many panels and many time periods, the storage requirements could be
> enormous.  Moreover, since -gmm- uses nonlinear optimization methods, it
> must evaluate the function many times.  As a result, -gmm- with panel-style
> instruments and 2,000,000 observations will be slow.
> 
>     -- Brian Poi
>     -- brian@poiholdings.com
> *
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