We characterize the optimal loss functions for predicted click-through rates in
auctions for online advertising. Whereas standard loss functions such as mean
squared error or log likelihood severely penalize large mispredictions while
imposing little penalty on smaller mistakes, a loss function reflecting the true
economic loss from mispredictions imposes significant penalties for small
mispredictions and only slightly larger penalties on large mispredictions. We
illustrate that when the model is misspecified using such a loss function can
improve economic efficiency, but the efficiency gain is likely to be small.