Journal of Econometrics· 2026Q1
Nonparametric identification of first-price auction with unobserved competition: A density discontinuity framework
- 4citations
- Q1SCImago
- 2026year
Short summary
This paper introduces a density discontinuity framework to nonparametrically identify first-price auction models where the number of bidders (N) is unobserved, using only winning bids.
AI-generated from the title and abstract; the full text is not read.
Key points
- Develops a nonparametric identification strategy for first-price auctions using only winning bids.
- Leverages discontinuities in the winning bid density to identify the distribution of the number of bidders (N).
- Allows for probabilistic bidding and additive parametric unobserved heterogeneity.
- Identifies the private value distribution in a second step after identifying N.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source. Journal of Econometrics, 2026 · DOI ↗
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Field: Management Science and Operations Research
Management Science and Operations ResearchDecision Sciences