I have an ordinal dependent variable. (I am looking at state bond ratings.) It ranges from 1 to 9. The 1st quantile value is 7-- so it is highly skewed with very few low values.

I understand some prefer OLS over ordered logit because the coefficients are easier to interpret and OLS is fairly robust. However, it assumes each category has an equal distance apart.

In order to estimate my coefficients with an ordered logit, I have to collapse a few categories in my DV (and lose information). Specifically, my scale is now 1 to 4. The OLS results (with the collapsed DV or the 1 to 4 scale) are similar to the ordered logit's, however, when I estimate the parameters for my model using OLS with out collapsing the DV (or the 1 to 9 scale), the results are substantially different.

In your opinion, between the OLS or ordered logit models, which would you prefer? With the ordered logit I am losing information by combining the lower categories, but my dependent variable is ordinal.


1 Answer 1


Long posts have appeared on this site about this subject.

You do not have to collapse any categories when using ordinal regression, and you usually shouldn't. The only issue you'll run into is computer time if you have more than a few hundred distinct $Y$ values. However in the R rms package orm function you can efficiently fit thousands of intercepts, just as you can in JMP.

I don't feel that OLS is very robust. Ordinal (semiparametric) regression is much more robust, and makes no distributional assumption for any one set of predictor values.


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