Abstract
We develop a new econometric approach for estimation of second-price ascending-bid auctions with a stochastic number of bidders. Our empirical framework considers the arrival process of new bidders as well as the distribution of bidders’ valuations of objects being auctioned. By observing the timing of bidder arrival the model is identified even when the number of potential bidders is stochastic and unknown. The relevance of our approach is illustrated with an empirical application using a unique dataset of art auctions on eBay. Our results suggest a higher impact of sellers’ reputation on bidders’ valuations than previously reported in cross-sectional studies but the impact of reputation on bidder arrival is largely insignificant. Interestingly, a seller’s reputation impacts not only the actions of the bidders but the actions of the seller as well. In particular, experience and a good reputation increase the probability of a seller posting items for sale on longer-lasting auctions which we find increases the expected revenue for the seller.
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