Author:

Publisher:

Published: 2014

Total Pages: 218

ISBN-13:

The first chapter studies identification and estimation of first-price auctions if the bidders face ambiguity about the distribution of valuations. Ambiguity is modeled using Gilboa and Schmeidler's (1989) Maxmin Expected Utility preferences. We exploit variation in the number of bidders to identify the essential primitives of the model. The identification result yields a closed form for the inverse bid function, which suggests a two-step estimation procedure. We study asymptotic and finite sample properties of the estimators. We find evidence of ambiguity in USFS timber auctions which leads to aggressive bidding for bidders with high valuations and has important implications for auction design. The second chapter proposes a procedure to test restrictions on infinite-dimensional parameters (partially) identified by unconditional or conditional moment equalities. Our new method allows us to test restrictions involving a continuum of inequalities. Examples of such restrictions include weakly increasing, concavity and first-order stochastic dominance. We show that our testing procedure controls size uniformly and has power approaching 1 against fixed alternatives. We conduct Monte Carlo Experiments to study the finite sample properties of our procedure. The third chapter studies the inference problem of bidders' risk attitudes in Independent Private Value (IPV) first-price auctions with multiplicative auction-level unobserved heterogeneity. Bidders are assumed to have Constant Relative Risk Aversion. Under the exclusion restriction that bidders randomly select themselves into auctions given the auction-level unobserved heterogeneity, bidders' CRRA coefficient is point-identified from bid data of auctions with at least two different number of active bidders. Our exclusion restriction is consistent with a variety of models with endogenous entry. Empirical application to USFS timber auctions shows that we will conclude that timber firms are risk averse if we ignoring the unobserved heterogeneity. But once we take the unobserved heterogeneity into account, risk neutrality is consistent with the data.