I am aware of some of the pitfalls of using banded versions of continuous variables when fitting a GLM (or other model), with a number of discussions on the subject on this forum (and a excellent list here: http://biostat.mc.vanderbilt.edu/wiki/Main/CatContinuous)
However, I’m interested to know what costs or benefits there may be in developing a model that only uses banded or binned versions of all continuous variables (with associated weighted mean bin values) and then adding the selected continuous variables later on the basis of the contribution made by their banded versions. .
I am asking as I am currently working in general insurance in the UK where a package that does just this is used extensively (it’s called Emblem and is part of a ‘pricing optimisation’ suite made by a financial services company). It does not accept any variables with more than 255 levels and so all continuous predictors have to be banded. The processed data files produced are very much smaller than their initial R or SAS counterparts and are then processed with an internal ‘R engine’.
The package allows models to be developed iteratively, adding and removing variables and interactions manually (often on the basis of expert knowledge, regulatory requirements etc.), re-fitting the model and then making a judgement on whether to keep them on the basis of graphs for each individual predictor and comparison with up to five saved ‘reference models’. Fitting times are very fast – a fraction of the time taken for the unbanded R or SAS equivalent.
All of this may be fine for a jobbing practitioner in the industry, but it got me to wondering if this approach might be appropriate for something more rigorous – what would be the pitfalls of building a model in this way and then replacing the banded versions with their original continuous values for a final evaluation for instance?