Estimating first-price auctions with an unknown number of bidders: A misclassification approach
Yingyao Hu and
Matthew Shum ()
Journal of Econometrics, 2010, vol. 157, issue 2, 328-341
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.
Keywords: Auction; models; Nonparametric; identification; Misclassification (search for similar items in EconPapers)
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Working Paper: Estimating First-Price Auctions with an Unknown Number of Bidders: A Misclassification Approach (2007)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:157:y:2010:i:2:p:328-341
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