# Estimation of Standard Auction Models

arXiv: https://arxiv.org/abs/2205.02060 · HTML: https://mlsanity.com/paper/2205.02060/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 31 references that are in the map.
- First posted on arXiv 4 May 2022.

## Builds on (most cited first, 2 of 2)

- [Multi-Item Mechanisms without Item-Independence: Learnability via Robustness](https://mlsanity.com/paper/1911.02146/) (2019)
- [Learning Valuation Distributions from Partial Observation](https://mlsanity.com/paper/1407.2855/) (2014)

## Built on by (most cited first, 2 of 2)

- [What Makes A Good Fisherman? Linear Regression under Self-Selection Bias](https://mlsanity.com/paper/2205.03246/) (2022)
- [Sample-Efficient Linear Regression with Self-Selection Bias](https://mlsanity.com/paper/2402.14229/) (2024)

## Abstract (arXiv)

We provide efficient estimation methods for first- and second-price auctions under independent (asymmetric) private values and partial observability. Given a finite set of observations, each comprising the identity of the winner and the price they paid in a sequence of identical auctions, we provide algorithms for non-parametrically estimating the bid distribution of each bidder, as well as their value distributions under equilibrium assumptions. We provide finite-sample estimation bounds which are uniform in that their error rates do not depend on the bid/value distributions being estimated. Our estimation guarantees advance a body of work in Econometrics wherein only identification results have been obtained, unless the setting is symmetric, parametric, or all bids are observable. Our guarantees also provide computationally and statistically effective alternatives to classical techniques from reliability theory. Finally, our results are immediately applicable to Dutch and English auctions.