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

Matthew Gentry, Tong Li and Jingfeng Lu

No 16/15, CeMMAP working papers from Institute for Fiscal Studies

Abstract: 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.

Date: 2015-04-01
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Citations: View citations in EconPapers (1)

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Working Paper: Identification and estimation in first-price auctions with risk-averse bidders and selective entry (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:azt:cemmap:16/15

DOI: 10.1920/wp.cem.2015.1615

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