When selecting between credit models, which ofthe following factors is least important?
A. That the model's parameter estimates are linear.
B. How easy the models are to understand.
C. How robust the models are when new data are added into the analysis.
D. The time to calibrate and recalibrate the model.
Answer:A
It is important for models to be understandable, robust and able to be recalibrated. Models do not have to be linear. However, a researcher may wish to use an easily understood model, yet she may have to choose a model that is very complex and takes a long time to calibrate because it gives the most accurate results.