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st: Re: RE: xtabond and rsquare goodness of fit measure


From   "Scott Merryman" <smerryman@kc.rr.com>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: Re: RE: xtabond and rsquare goodness of fit measure
Date   Fri, 15 Aug 2003 09:09:54 -0500

----- Original Message -----
From: "Nick Cox" <n.j.cox@durham.ac.uk>
To: <statalist@hsphsun2.harvard.edu>
Sent: Friday, August 15, 2003 8:48 AM
Subject: st: RE: xtabond and rsquare goodness of fit measure


> Parthiban David
> >
> > I estimated a regression model using xtabond in stata. A
> > referee wants us to
> > report a goodness of fit measure (e.g., rsquare or pseudo
> > rsquare). I
> > checked the stata archives (using findit rsquare) but could not find
> > anything specific for xtabond. The closest was an faq by
> > William Gould which
> > presents a procedure to compute pseudo Rsquare for probit.
> >
> > Does anyone know of an equivalent way to compute a pseudo
> > Rsquare for
> > xtabond?
> >
> > I noticed that xtabond reports chi-squared statistics for
> > the variables
> > present in the model. Does it make sense to compute a
> > goodness of fit by
> > running two separate xtabond regressions using a full model
> > and reduced
> > model and comparing the chi-squared statistics?
>
> This may be stupid in view of specifics of -xtabond-, but
> when all else fails one R^2 measure is always obtainable as
> the square of the correlation between the observed and
> the predicted, the latter being always calculated
> in the same form as the response. In other words,
> use -predict- to get predicted, and then -correlate- and
> finally square.
>
> Make sure you restrict calculation to the estimation sample.
>
> This is how I would do it for -regress-
>
> regress <response> <whatever>
> predict predict if e(sample)
> correlate <response> predict
> di %23.18f (r(rho))^2
>
> Nick
> n.j.cox@durham.ac.uk
>

This is the goodness of fit measure that Bloom, Bond, and Van Reenen use in "The
Dynamics of Investment under Uncertainty"
(http://www.staff.city.ac.uk/~giourga/CP2.pdf) using using firm-level panel data
and GMM estimates of dynamic investment equations.


Scott




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