Dear Christopher,
Thanks for the reply. I too was suspicious of using SURE.
As I have different number of obs in every sample it
makes it stronger the argument of not using it. I guess I
have to pool the sample to test for coefficient differences.
It will however introduce endogeneity in the estimation,
exactly the reason why I had to split the sample.
Sincerely,
Bersant
----- Original Message -----
From: Christopher F Baum <[email protected]>
Date: Friday, June 27, 2003 12:25 pm
Subject: st: Re: coeffs across equations
> On Friday, June 27, 2003, at 02:33 AM, Scott wrote in
response to
> Bersant:
>
> > Date: Thu, 26 Jun 2003 21:00:53 -0500
> > From: "Scott Merryman" <[email protected]>
> > Subject: Re: st: test coefficients across equations
[was: Re:
> sample
> > selection bias]
> >
> > - ----- Original Message -----
> > From: "Bersant Hobdari" <[email protected]>
> > To: <[email protected]>
> > Sent: Thursday, June 26, 2003 11:51 AM
> > Subject: Re: st: Re: sample selection bias
> >
> >
> >> Hi Everyone,
> >>
> >> I had a question on testing coefficient across
separately estimated
> >> samples. The problem is the following: I estimate firm-
level
> >> production
> >> function where I divide the sample in 5 sub-samples
defined by
> >> majority
> >> owner: I.e., if majority owner is the State I classify the
firm
> in
> >> that
> >> group, if it is a financial institution I classify it in that
> group
> >> and
> >> so on. After estimating regressions I would like to test
the
> equality
> >> of
> >> coefficients across equations.
> >>
> >> Any suggestion how this could be implemented is
highly appreciated.
> >>
> >> Sincerely,
> >> Bersant Hobdari
> >
> >
> > You could create a dummy variable on majority owner
then interact
> it
> > with
> > your other variables and test the coefficients on the
fully
> interacted
> > model
> > (see the Stata FAQ on Chow tests). Example using
the auto dataset.
> >
> > Equation 1: mpg = b0 + b1*price (if domestic)
> > Equation 2: mpg = b0' + b1'*price (if foreign)
> >
> > Create the interaction term (if you have more
categories -xi-
> comes in
> > handy)
> > gen priceXforeign = price *foreign
> >
> > Regress the full interacted model
> > regress mpg = price foreign priceXforeign
> >
> > A test on foreign will compare common intercepts, a
test on
> > priceXforeign
> > will test common slopes, and a test on both foreign and
> priceXforeign
> > will
> > test if they are jointly equal to zero, or if equation 2
differs
> from> equation 1.
> >
> > However, if you are concerned about correlation
across equation
> (or
> > wish to
> > test for it), -reshape- your data into a wide data
structure and
> > use -sureg-.
> >
> >
> This is a FAQ on Statalist. One cannot in general test
coefficients
> from separate estimations. Pooling the data and
estimating a
> single,
> fully interacted model, as suggested, is probably the
most sensible
> approach, although it does then assume
homoskedasticity across the
> 5
> categories, which 5 separate regressions will not. Thus,
one may
> want
> to test for groupwise heteroskedasticity if the est.
sigma^2 from
> the
> category regressions are very different.
>
> The suggestion for sureg relaxes that assumption, and
handles the
> separate coefficients (including making it easy to test /
constrain
> across equations). However most implementations of
SURE will demand
> that there are the same number of observations in each
equation (a
> constraint not required by SURE, in that one can
calculate
> consistent
> estimates of the residual covariance matrix from the dot
products
> of
> the residuals, whatever their length). I suspect Bersant
may not
> have
> equal numbers of firms per category.
>
> Kit
>
> *
> * For searches and help try:
> * http://www.stata.com/support/faqs/res/findit.html
> * http://www.stata.com/support/statalist/faq
> * http://www.ats.ucla.edu/stat/stata/
>
>
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n:Hobdari;Bersant
fn:Bersant Hobdari
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email;internet:[email protected]
end:vcard
