# Is Modeling an Independent Variable the Best Approach?

I am developing an early-warning GLM that predicts insurance claim severity based on characteristics such as industry, age, type of injury, etc. One of the fields that has proved most predictive, Variable X, is not known with certainty early enough in the life of the claim for me to use it in the model.

Would it be appropriate to develop a GLM to predict variable X, and then use this prediction as an input into the original early-warning model? If so, would I re-model the early-warning model with the predicted variable X data or use the original model coefficients based on the actual variable X data?

• Sych model would not predict X but E(X) (see here), so you won't see any extreme values in the predicted X -- I guess that in early warning system you would be interested in the extreme values..? – Tim Nov 10 '16 at 15:22
• Trying to identify the top 20% of claims, so relatively extreme, yes. – Frank H. Nov 10 '16 at 15:31
• Why not just include any additional variables used for predicting $X$ directly within your early-warning GLM? – whuber Nov 10 '16 at 16:25
• @whuber - Both models use the same variables but the ways each variable are categorized or transformed vary by model. Said another way, I tried just excluding variable X and the early-warning model loses significant predictive value. – Frank H. Nov 10 '16 at 17:57
• You asked for liks ... This is relevant to the multi-stage bootstrapping that I had previously referred to .... stats.stackexchange.com/questions/240728/… – IrishStat Nov 10 '16 at 22:47