Empirical Relevance of Ambiguity in First Price Auction Models

TL;DR

This paper investigates how ambiguity affects first-price auction models, providing identification, estimation methods, and demonstrating the importance of accounting for ambiguity to avoid bias and revenue loss.

Contribution

It introduces a nonparametric identification approach and a Bayesian estimation method for auction models with ambiguity, highlighting the impact of misspecification.

Findings

Bayesian method estimates parameters precisely

Reserve prices are nearly optimal with the proposed method

Ignoring ambiguity can cause significant revenue loss

Abstract

We study the identification and estimation of first-price auction models where bidders have ambiguity about the valuation distribution and their preferences are represented by maxmin expected utility. When entry is exogenous, the distribution and ambiguity structure are nonparametrically identified, separately from risk aversion (CRRA). We propose a flexible Bayesian method based on Bernstein polynomials. Monte Carlo experiments show that our method estimates parameters precisely, and chooses reserve prices with (nearly) optimal revenues, whether there is ambiguity or not. Furthermore, if the model is misspecified -- incorrectly assuming no ambiguity among bidders -- it may induce estimation bias with a substantial revenue loss.