Online auctions have recently gained widespread popularity and are one of the most successful forms of electronic commerce. We examine a dataset of eBay coin auctions to explore features of online bidding and selling behavior. We address three main issues. First, we measure the extent of the winner's curse. We find that for a representative auction in our sample, a bidder's expected profits fall by 3.2 percent when the expected number of bidders increases by one. Second, we document that costly entry is a key component in understanding observed bidding behavior. For a representative auction in our sample, a bidder requires $3.20 of expected profit to enter the auction. Third, we study the seller's choice of reserve prices. We find that items with higher book value tend to be sold using a secret as opposed to posted reserve price with a low minimum bid. We find that this is, to a first approximation, consistent with maximizing behavior. We also develop new techniques for structurally estimating common value auction models.
We describe a two-step algorithm for estimating dynamic games under the assumption that behavior is consistent with Markov perfect equilibrium. In the first step, the policy functions and the law of motion for the state variables are estimated. In the second step, the remaining structural parameters are estimated using the optimality conditions for equilibrium. The second step estimator is a simple simulated minimum distance estimator. The algorithm applies to a broad class of models, including industry competition models with both discrete and continuous controls such as the Ericson and Pakes (1995) model. We test the algorithm on a class of dynamic discrete choice models with normally distributed errors and a class of dynamic oligopoly models similar to that of Pakes and McGuire (1994).
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