Identification in Auctions with Truncated Transaction Prices
成交价格截断下的拍卖识别AI 解读 ↓
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成交价格截断下的拍卖识别AI 解读 ↓
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I establish nonparametric identification results in first- and second-price auctions when transaction prices are truncated by a binding reserve price under a range of information structures. When the number of potential bidders is fixed and known across all auctions, if only the transaction price is observed, the bidders' private-value distribution is identified in second-price auctions but not in first-price auctions. Identification in first-price auctions can be achieved if either the number of active bidders or the number of auctions with no sales is observed. When the number of potential bidders varies across auctions and is unknown, the bidders' private-value distribution is identified in first-price auctions but not in second-price auctions, provided that both the transaction price and the number of active bidders are observed. I derive analogous results to auctions with entry costs, which face a similar truncation issue when data on potential bidders who do not enter are missing.
未观测竞争下的第一价格拍卖非参数识别:密度断点框架
We consider nonparametric identification of independent private value first-price auction models, in which the analyst only observes winning bids. Our benchmark model assumes an exogenous number of bidders N. We show that, if the bidders observe N, the resulting discontinuities in the winning bid density can be used to identify the distribution of N. The private value distribution can be nonparametrically identified in a second step. This extends, under testable…
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We first provide inference on the quantile function of the bid distribution in firstprice or descending auctions, and the quantile function of the value distribution in second-price or ascending auctions, with a known number of bidders with symmetric independent private values. Specifically, we provide finite-sample, nonparametric, tuning parameter-free, uniform confidence bands for these quantile functions when observing only the transaction price. We also show how these…
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未观测竞争下的第一价格拍卖非参数识别:密度不连续框架
We consider nonparametric identification of independent private value first-price auction models, in which the analyst only observes winning bids. Our benchmark model assumes an exogenous number of bidders N. We show that, if the bidders observe N, the resulting discontinuities in the winning bid density can be used to identify the distribution of N. The private value distribution can be nonparametrically identified in a second step. This extends, under testable…
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