Open AccessIdentification in ascending auctions, with an application to digital rights management
Author(s) -
Freyberger Joachim,
Larsen Bradley J.
Publication year - 2022
Publication title -
quantitative economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 4.062
H-Index - 27
eISSN - 1759-7331
pISSN - 1759-7323
DOI - 10.3982/qe1151
Subject(s) - common value auction , identification (biology) , nonparametric statistics , order (exchange) , econometrics , point (geometry) , inference , estimation , economics , point estimation , business , microeconomics , computer science , statistics , mathematics , finance , botany , geometry , management , artificial intelligence , biology
This study provides new identification and estimation results for ascending (traditional English or online) auctions with unobserved auction‐level heterogeneity and an unknown number of bidders. When the seller's reserve price and two order statistics of bids are observed, we derive conditions under which the distributions of buyer valuations, unobserved heterogeneity, and number of participants are point identified. We also derive conditions for point identification in cases where reserve prices are binding and present general conditions for partial identification. We propose a nonparametric maximum likelihood approach for estimation and inference. We apply our approach to the online market for used iPhones and analyze the effects of recent regulatory changes banning consumers from circumventing digital rights management technologies used to lock phones to service providers. We find that buyer valuations for unlocked phones dropped by 39% on average after the unlocking ban took effect, from $231.30 to $141.50.