Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/49868 
Year of Publication: 
2007
Series/Report no.: 
Working Paper No. 541
Publisher: 
The Johns Hopkins University, Department of Economics, Baltimore, MD
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.
Document Type: 
Working Paper

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