(I feel like if you're active here, you've come across my problem before because I've been asking a lot...)
I want to run a regression, in the area of credit risk in loans, to predict the outcome of a response variable with 3 categories:
-self cure (2)
-not self cure (1)
-not cured/default (0)
I found it hard to use an ordinal logit before, so I went for multinomial logit (where the order doesn't really matter), but now I'm doubting if I didn't think it through enough.
Might be a strange question to ask but, is there a big risk of choosing to run a multinomial model instead of an ordinal one? I feel like, if it was the other way around it would be quite a mistake if the response variable isn't actually ordinal, because the proportional odds are not met, so I wondered if it would be equally wrong to go for multinomial.
Here are a few explanations I have to choose multinomial: If the response variable was ordinal, following its current order, it would mean that 0 or not-cured is the base level, and if a client is in level 1 or NSC, it exceeded the base level, which is partly true if we think that curing is better or “higher” than not curing. But then if a client is in level 2, or self-cured, it would have exceeded the base level and level 1, which does not make much sense in this case because NSC and self-cured are exclusive.
Also, there's no particular order for the classes... it could be: 0 - self cured / 1 - not self cured / 2 - not cured
0 - not self cured / 1 - self cured / 2 - not cured
Does that makes sense?