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On Jun 30, 1:10 pm, "Victor " <victorgen...@hotmail.fr> wrote:
> Good afternoon,
>
> I am attempting to model sovereign default probabilities. To do so I have as dependant variable:
> y=[0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1]
> and four explanatory variables x1 x2 x3 and x4, all of same dimension.
>
> Here are my issues:
> 1) Shall I regress first my explanatory variables against the variable Y (which is not in a binary form) from which I derived y? Or should I directly regress these explanatory variables against y?
>
> If the second option is the right one, how do I do that?
>
> 2) I do not really know what are the steps I should follow then to obtain the probability of default, according to the variable that I will find significant.
>
> Assuming x1 and x2 are significant, are the following steps correct?
>
> X=[x1 x2]
> [b,dev,stats] = glmfit(X,y,'binomial','link','logit')
> yfit = glmval(b, X,'logit');
>
> 3) Finally, if the above steps are correct, among the outputs returned, which one would give me the probability of default?
yfit.
help glmfit
doc glmfit
Hope this helps.
Greg
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