Estimation of Standard Auction Models

TL;DR

This paper introduces efficient, non-parametric estimation methods for auction models with private values, providing finite-sample bounds and applicability to various auction types, advancing econometric analysis.

Contribution

It offers the first finite-sample, non-parametric estimation algorithms with uniform error bounds for asymmetric private-value auctions, surpassing previous identification-only results.

Findings

Provides algorithms for bid and value distribution estimation.

Establishes finite-sample error bounds independent of distributions.

Applicable to Dutch and English auction formats.

Abstract

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.