# st: RE: RE: multicollinearity test for probit?

 From "Joao Pedro W. de Azevedo" <[email protected]> To <[email protected]> Subject st: RE: RE: multicollinearity test for probit? Date Wed, 10 Dec 2003 23:30:01 -0200

```Dear Matt,

Another suggestion might be to apply the following tests:

Bartlett's test for sphericity; and,

Both tests are often used prior to PCA or Factor Analysis and test precisely
to which extent the variables used in the model are correlated.

These tests can be implemented in STATA through the ado file FACTORTEST

. ssc install factortest

Cheers,
JP

-----Original Message-----
From: [email protected]
[mailto:[email protected]] On Behalf Of Nick Cox
Sent: Wednesday, December 10, 2003 8:12 PM
To: [email protected]
Subject: st: RE: multicollinearity test for probit?

As I understand it, multicollinearity on the right-hand side
is much the same irrespective of what is on the left-hand
side or what link function is contemplated. So I don't
see that you are obliged to do it retrospectively.

The world divides here into those who think in terms of
some test (e.g. through an omnibus or portmanteau test statistic)
and those who want to examine structure or look for potential
problems in an exploratory way. As someone firmly in the
latter camp, I've no idea if there's some overall test
which supposedly maps all pertinent information into a single
statistic. If they tell you there is one, its merits
are probably exaggerated.

Three things spring to mind. There are probably thirty others,
and my three may not even be among the most important.

1. -_rmcoll-
============

Check it out.

2. -pca-
========

One of the best general ways of looking for structure
is to look at the principal components of the predictors.
The eigenvalues give a quick guide to whether you have clusters of
variables. Perhaps the best single diagnostic is not
among the standard outputs: it is the correlations
between the original variables and the components.
-makematrix- on SSC gives a relatively painless
way of getting those concisely and directly.

Sometimes you look at the principal components
and see some structure that you then realise
could be presented and explained in some other
way, say directly in terms of the correlation
matrix. It's like climbing up the North Face
with pitons and ropes and goodness knows what,
but when you get to the top you see that there
was an easier way to climb up there all the time.
(I've never done this, but I saw Clint Eastwood do it once.
Principal components, that is.)

3. scatterplot matrix
=====================

This can be a good way of scanning a few dozen
predictors. You are just on the look out for scatter plots
with strongly diagonal patterns. (But watch out
for outliers too.)

Nick
[email protected]

Matt A. Barreto

> Is there a similar command to vif following regress when
> using probit or
> oprobit (or logit/ologit) to test for multicollinearity
> among independent
> variables in a probit equation?

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```