Colliniarity is an issue among your predictor
variables--not your dependent variable. Hence, you can
simply fit an OLS model--and examine your conditions
indexes and VIFs to examine the extent of
multicollinearity among your predictor variables.
SR Millis
--- "Yatawara, Ravi" <[email protected]> wrote:
> Folks,
> I have a dependent variable which is 3 possible
> trade policy stances.
> (status quo, liberalize or increase protection) for
> 40 countries for 30
> years each.
>
> The independent variables are alternate forms of
> crisis and a political
> variable for government change. The govt change
> variable is
> statistically significant- the crisis variables are
> not.
>
> The issue with a referee is that maybe crisis is
> causing the government
> change.
>
> I have dropped govt change , and the crisis
> variables do not become
> significant.
> I estimated a logit model of govt change using the
> crisis variables as
> independent variables and carried out an F test to
> determine
> collinaerity
> Fk-2,n-k+1= [R squared/(k-2)]/ [1-R squared/(n-k+1)
>
> Where k= numberof explanatory variables including
> intercept.
>
> My questions are 1) is this sufficient under a
> multinomial framework and
> 2) are there better altenatives I should
> investigate.
>
> Any feedback will be much appreciated!
> Thanks
> Ravi
>
>
> *
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> *
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>
Scott R Millis, PhD, MEd, ABPP (CN & RP)
Director of Research - Dept of PM&R
Wayne State University School of Medicine
Rehabilitation Institute of Medicine
261 Mack Blvd
Detroit, MI 48201
Email: [email protected]
Tel: 313-993-8085
Fax: 313-745-9854
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