Identification and estimation of a bidding model for electronic auctions

Because of discrete bid increments, bidders at electronic auctions engage in shading instead of revealing their valuations, which would occur under the commonly assumed second-price rule. We demonstrate that misspecifying the pricing rule can lead to biased estimates of the latent valuation distribution, and then explore identification and estimation of a model with a correctly specified pricing rule. A further challenge to econometricians is that only a lower bound on the number of participants at each auction is observed. From this bound, however, we establish nonparametric identification of the arrival process of bidders-the process that matches potential buyers to auction listings - which then allows us to identify the latent valuation distribution without imposing functional-form assumptions. We propose a computationally tractable, sieve-type estimator of the latent valua- tion distribution based on B-splines, and then compare two parametric models of bidder participation, finding that a generalized Poisson model cannot be rejected by the empirical distribution of observables. Our structural estimates enable us to explore information rents and optimal reserve prices on eBay.