Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5096928 | Journal of Econometrics | 2010 | 14 Pages |
Abstract
In this paper, we consider nonparametric identification and estimation of first-price auction models when Nâ, 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 Nâ. 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 Nâ can lead to economically meaningful differences in the estimates of bidders' profit margins.
Related Topics
Physical Sciences and Engineering
Mathematics
Statistics and Probability
Authors
Yonghong An, Yingyao Hu, Matthew Shum,