Arbeitspapier

Identification and estimation in first-price auctions with risk-averse bidders and selective entry

We study identification and estimation in first-price auctions with risk-averse bidders and selective entry, building on a flexible entry and bidding framework we call the Affiliated Signal with Risk Aversion (AS-RA) model. This framework extends the AS model of Gentry and Li (2014) to accommodate arbitrary bidder risk aversion, thereby nesting a variety of standard models as special cases. It poses, however, a unique methodological challenge - existing results on identification with risk aversion fail in the presence of selection, while the selection-robust bounds of Gentry and Li (2014) fail in the presence of risk aversion. Motivated by this problem, we translate excludable variation in potential competition into identified sets for AS-RA primitives under various classes of restrictions on the model. We show that a single parametric restriction - on the copula governing selection into entry - is typically sufficient to restore point identification of all primitives. In contrast, a parametric form for utility yields point identification of the utility function but only partial identification of remaining primitives. Finally, we outline a simple semiparametric estimator combining Constant Relative Risk Aversion utility with a parametric signal-value copula. Simulation evidence suggests that this estimator performs very well even in small samples, underscoring the practical value of our identification results.

Sprache
Englisch

Erschienen in
Series: cemmap working paper ; No. CWP16/15

Klassifikation
Wirtschaft
Thema
Auctions
endogenous participation
risk aversion
identification

Ereignis
Geistige Schöpfung
(wer)
Gentry, Matthew
Li, Tong
Lu, Jingfeng
Ereignis
Veröffentlichung
(wer)
Centre for Microdata Methods and Practice (cemmap)
(wo)
London
(wann)
2015

DOI
doi:10.1920/wp.cem.2015.1615
Handle
Letzte Aktualisierung
10.03.2025, 11:43 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • Gentry, Matthew
  • Li, Tong
  • Lu, Jingfeng
  • Centre for Microdata Methods and Practice (cemmap)

Entstanden

  • 2015

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