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We study the identification and estimation of first-price auction modelswhere bidders have ambiguity about the valuation distribution and theirpreferences are represented by maxmin expected utility. When entry isexogenous, the distribution and ambiguity structure are nonparametricallyidentified, separately from risk aversion (CRRA). We propose a flexibleBayesian method based on Bernstein polynomials. Monte Carlo experiments showthat our method estimates parameters precisely, and chooses reserve prices with(nearly) optimal revenues, whether there is ambiguity or not. Furthermore, ifthe model is misspecified -- incorrectly assuming no ambiguity among bidders --it may induce estimation bias with a substantial revenue loss.

Empirical Relevance of Ambiguity in First Price Auction Models