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 valuation 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.