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Bounds on revenue distributions in counterfactual auctions with reserve prices
Authors:Xun Tang
Institution:1. University of Pennsylvania;2. xuntang@econ.upenn.edu.
Abstract:Measuring auction revenues under counterfactual reserve prices or formats requires knowledge of distributions of bidders' values and private signals. This poses a challenge when bids are observed from first‐price, common‐value auctions. I bound counterfactual revenue distributions without imposing parametric restrictions on the model structure. Using data from U.S. municipal bond auctions, I find first‐price and second‐price auctions under optimal reserve prices lead to little improvement in revenues over existing first‐price formats. The number of bidders has a more significant impact on revenues in optimal auctions. I also find invoking an incorrect assumption of private values in counterfactual analyses results in small errors in predicting revenues from optimal second‐price auctions.
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