Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/296281 
Year of Publication: 
2022
Citation: 
[Journal:] Quantitative Economics [ISSN:] 1759-7331 [Volume:] 13 [Issue:] 2 [Year:] 2022 [Pages:] 505-543
Publisher: 
The Econometric Society, New Haven, CT
Abstract: 
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.
Subjects: 
Ascending auctions
nonparametric identification
unobserved het-erogeneity
unknown number of bidders
sieve maximum likelihood
digital rights
Digital Millennium Copyright Act
grey-market activity
smartphone unlocking
JEL: 
C10
D44
K11
K24
L10
L96
O34
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc Logo
Document Type: 
Article

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