I am interested in modeling the probability of default (PD) of a loan product.
- I have a dataset going back several years. Most of the loans have reached their terminal state (paid off or default) but there is a considerable number that are still active.
- Each observation represents a loan.
- The dependent variable represents whether a loan paid off, defaulted, or is active as of the date the dataset was created.
- There are also variables which I indend to use as explanatory factors.
- The age of the loan at the time it paid off/defaulted is unavailable
If I model the probability of default using a logistic regression over the entire dataset, how should I treat the currently active loans? Should they be excluded from the training dataset, or modeled as a non-default state?