st: RE: condivreg/Multicollinearity

["Vassilopoulos Achilleas" <vassilopoulos.statalist@gmail.com>]

If your dummies are collinear, they will be dropped no matter which model
you employ.  
As a result, a simple way to get what you need (not very elegant though) is
to run a -reg- or -probit- or -logit- with all your dummies as independents
and generate a global or local with the names in the coefficient vector i.e.
: 

reg DEPENDENT dummy1 dummy2 ..... dummy500

global list : colnames e(b)  or 

local list : colnames e(b)

Then you can use this $list or `list' in your subsequent estimations...

Hope this helps,
_____________ - _______________

Achilleas Vassilopoulos

Agricultural University of Athens,
Dept. of Agricultural Economics and Rural Development,
Lab. of Political Economy and European Integration.
Iera Odos 75, 11855, Athens, Greece

Tel: (+30) 210-5294726
Fax: (+30) 2105294786
e-mail : avassilopoulos.aua@gmail.com   

-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of richard boylan
Sent: Wednesday, September 08, 2010 10:14 PM
To: statalist@hsphsun2.harvard.edu
Subject: st: condivreg/Multicollinearity

I am estimating a model with a very large number of interacting dummy
variables in the
first stage (> 500); so it is difficult a priori to determine which
dummy variables are going
to be collinear.

When I estimate the model with ivreg, STATA eliminates a variety of
collinear variables
and produces estimates.  However, when I use condivreg I get an error
message

r(498);

and a message "Multicollinearity!"

It seems to me that one way of trying to solve this problem is to
obtain from ivreg the list
of variables that were not dropped in the first stage, and then
estimate condivreg using
the same variables.

If this seems like a reasonable way of going about it, can anyone
point me about how
I would go about doing that? I.e., putting in a local variable the
list of variables that
were used in the first stage?
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