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Re: st: Multicollinearity in a simple Poisson model


From   "maartenbuis" <maartenbuis@yahoo.co.uk>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Multicollinearity in a simple Poisson model
Date   Mon, 26 Sep 2005 12:08:25 -0000

Hi Alice:

Say we have two explanatory variables, x1 and x2, and these variables 
are highly positively correlated. So whenever we see a high value of 
x1 we also see a high value of x2. Now how can we say that a change 
in y is caused by either x1 or x2? We obviously can't distinguish 
between the effect of x1 and x2 if the correlation between x1 and x2 
is perfect. If the correlation is close to 1 (or -1) it is extremely 
difficult, and the confidence intervals will reflect that by being 
very large. The estimates will also tend to be unstable, i.e. small 
changes in your data or model will lead to large changes in the 
estimates. This is true for any type of multiple regression, 
including OLS and Poisson. So using poisson regression will not solve 
your multicolinearity problem.

Hope this helps,
Maarten

-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-
statalist@hsphsun2.harvard.edu]On Behalf Of ALICE DOBSON
Sent: maandag 26 september 2005 11:45
To: statalist@hsphsun2.harvard.edu
Subject: Re: st: Multicollinearity in a simple Poisson model

Because multicollinearity may influence the least squares estimates. 
This is one of the basic assumptions in OLS. However, I have not come 
across such an assumption for Poisson regression [which assumes a 
Poisson distribution]. This may be due to my limited exposure to 
statistics literature.





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