  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.
