Identification and Inference in First-Price Auctions with Risk Averse Bidders and Selective Entry
75 Pages Posted: 15 Oct 2020 Last revised: 7 Jun 2023
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Identification and Inference in First-Price Auctions with Risk-Averse Bidders and Selective Entry
Date Written: May 28, 2023
Abstract
We study identification and inference in first-price auctions with risk averse bidders and selective entry, building on a flexible framework we call the Affiliated Signal with Risk Aversion (AS-RA) model with potentially non-binding reserve prices. Assuming either exogenous variation in the number of potential bidders (N) or a continuous instrument (z) shifting opportunity costs of entry, we provide a sharp characterization of the nonparametric restrictions implied by equilibrium bidding. This characterization implies that risk neutrality is nonparametrically testable. In addition, with sufficient variation in both N and z, the AS-RA model primitives are nonparametrically identified (up to a bounded constant) on their equilibrium domains. Finally, we explore new methods for inference in set-identified auction models based on Chen, Christensen, and Tamer (2018), as well as novel computational strategies to implement these. Simulation studies reveal good finite-sample performance of our inference methods, which can readily be adapted to other set-identified auction models.
Keywords: Auctions, entry, risk aversion, identification, set inference, MPEC, profile likelihood ratio, nonregular models.
JEL Classification: D44, C57
Suggested Citation: Suggested Citation