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Loss functions for predicted click‐through rates in auctions for online advertising
Author(s) -
Hummel Patrick,
McAfee R. Preston
Publication year - 2017
Publication title -
journal of applied econometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.878
H-Index - 99
eISSN - 1099-1255
pISSN - 0883-7252
DOI - 10.1002/jae.2581
Subject(s) - common value auction , computer science , function (biology) , econometrics , click through rate , online advertising , advertising , economics , microeconomics , the internet , world wide web , business , evolutionary biology , biology
Summary 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.