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Re: st: comparing coefficients across models


From   Dalhia <ggs_da@yahoo.com>
To   "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>
Subject   Re: st: comparing coefficients across models
Date   Fri, 3 Aug 2012 05:06:51 -0700 (PDT)

David,
Sorry about the confusion. Typo. 

Here is what I should have said:

This is how I ran the interaction model:
xtreg y x1 x2 x3 x3*group_dummy, fe robust


where y is log of Tobin's q (a measure of firm performance)
x1 is degree centrality (a network measure - continuous)
x2 is business group dummy (codes whether or not a firm belongs to a cluster of firms)
x3 is betweenness centrality (a network measure - continuous)
group_dummy is whether or not the firm belongs to a particular component in the network.

x2 and x3 are the most  highly correlated (-0.73).

Thanks. 
Dalhia


----- Original Message -----
From: David Hoaglin <dchoaglin@gmail.com>
To: statalist@hsphsun2.harvard.edu
Cc: 
Sent: Friday, August 3, 2012 4:37 AM
Subject: Re: st: comparing coefficients across models

Thanks, Dalhia.

I'm confused about the variables in your model.  Your list of
definitions doesn't include x1, and x3 appears twice.

David Hoaglin

On Thu, Aug 2, 2012 at 10:13 PM, Dalhia <ggs_da@yahoo.com> wrote:
>
> David, thanks. the previous post on this issue, and James' summary of it has been really helpful.
>
> To answer your question, I tried centering the variables but it doesn't help with collinearity. The reason is that I have 3 network variables in the model. The interaction term multiplies one of these with another network derived measure.
>
> Here is how I ran the model (with interaction term):
>
> xtreg y x1 x2 x3 x3*group_dummy, fe robust
>
> where y is log of Tobin's q (a measure of firm performance)
> x2 is degree centrality (a network measure - continuous)
> x3 is business group dummy (codes whether or not a firm belongs to a cluster of firms)
> x3 is betweenness centrality (a network measure - continuous)
> group_dummy is whether or not the firm belongs to a particular component in the network.
>
> x2 and x3 are the most  highly correlated (-0.73).
>
> Any thoughts are welcome. I really appreciate the help.
> Dalhia
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