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Estimating First-Price Auctions with an Unknown Number of Bidders: A Misclassification Approach

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  • Yingyao Hu
  • Matthew Shum

Abstract

In this paper, we consider nonparametric identification and estimation of first-price auction models when N*, the number of potential bidders, is unknown to the researcher, but observed by bidders. Exploiting results from the recent econometric literature on models with misclassification error, we develop a nonparametric procedure for recovering the distribution of bids conditional on the unknown N*. Monte Carlo results illustrate that the procedure works well in practice. We present illustrative evidence from a dataset of procurement auctions, which shows that accounting for the unobservability of N* can lead to economically meaningful differences in the estimates of bidders' profit margins.

Suggested Citation

  • Yingyao Hu & Matthew Shum, 2007. "Estimating First-Price Auctions with an Unknown Number of Bidders: A Misclassification Approach," Economics Working Paper Archive 541, The Johns Hopkins University,Department of Economics.
    • Handle: RePEc:jhu:papers:541
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