Identification in Ascending Auctions, with an Application to Digital Rights Management
This study provides new identication 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 identied. We also derive conditions for point identication in cases where reserve prices are binding and present general conditions for partial identication.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.
unknown number of bidders
sieve maximum likelihood
Digital Millennium Copyright Act