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Nonparametric Identification of First-Price Auction with Unobserved Competition: A Density Discontinuity Framework

Emmanuel Guerre, Yao Luo

arXiv 15 Aug 2019 · Econometrics · 4 citations (OpenAlex)

arXiv:1908.05476 · PDF · DOI · OpenAlex · Extracted main text

Abstract

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 identification conditions, to the case where $N$ is a number of potential buyers, who bid with some unknown probability. Identification also holds in presence of additive unobserved heterogeneity drawn from some parametric distributions. A parametric Bayesian estimation procedure is proposed. An application to Shanghai Government IT procurements finds that the imposed three bidders participation rule is not effective. This generates loss in the range of as large as $10%$ of the appraisal budget for small IT contracts.

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appendix boundary found by appendix_titled_section at “Appendix A: Simulation experiments” · 70% of the source is main text. Read the extracted text to check this.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Inference in Auctions with Many Bidders Using Transaction Prices0.87452
2Identification of Auction Models Using Order Statistics0.81142
3Estimating Nonseparable Selection Models: A Functional Contraction Approach0.51121