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Re: st: Detecting collinearity during regression analysis

From   Steven Samuels <>
Subject   Re: st: Detecting collinearity during regression analysis
Date   Tue, 10 Feb 2009 16:10:13 -0500

You are trying to fit 75 parameters with only 74 observations. This is not collinearity, but over-fitting. I suggest you consult a good text on regression.

On Feb 10, 2009, at 2:28 PM, Anon Mouse wrote:

Hello and thank you in advance,

I have a question about detecting collinearity.

First, see my example:


sysuse auto, clear
xi: qreg mpg foreign i.make, nolog


Note in this example that I used quantile regression to determine effects of foreign and make on MPG.

I used xi command to create dummy categorical variables for make (note that this creates quite a large number of variables for make, as make is a continuous variable, but I did this for example).

Note that the coefficients are very small (e.g. e^-15), approaching zero.

In this example, does this indicate collinearity?  And why?

The reason I used such a granular dummy categorization for make is to highlight my example. In my real data, I have age and wage. When I use categories such as age/10 or wage/10000, this gives me "collinearity" (i.e. very small coefficients). When I collapse these age or wage categories to smaller categories (i.e. age or wage as binomial variables, greater than or less than a certain value), I correct this problem of "collinearity".

Am I correct in my assumptions?

Thank you.

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