Identification of Auction Models Using Order Statistics

TL;DR

This paper develops identification methods for auction models using only order statistics, addressing dependence issues and unobserved heterogeneity in symmetric and ascending auctions.

Contribution

It introduces novel identification results for auction models based on order statistics, including discrete unobserved heterogeneity and unknown competition.

Findings

Symmetric auctions with discrete heterogeneity are identifiable with two order statistics and an instrument.

Results extend to ascending auctions with unknown competition and heterogeneity.

Provides a framework for identifying auction models from limited bid data.

Abstract

Auction data often contain information on only the most competitive bids as opposed to all bids. The usual measurement error approaches to unobserved heterogeneity are inapplicable due to dependence among order statistics. We bridge this gap by providing a set of positive identification results. First, we show that symmetric auctions with discrete unobserved heterogeneity are identifiable using two consecutive order statistics and an instrument. Second, we extend the results to ascending auctions with unknown competition and unobserved heterogeneity.