Nonparametric identification of an interdependent value model with buyer covariates from first-price auction bids

TL;DR

This paper develops a nonparametric identification method for an interdependent value auction model with buyer covariates, enabling analysis of auction outcomes and counterfactuals using first-price bid data.

Contribution

It introduces a novel nonparametric identification approach for interdependent value models with covariates from first-price auction bids, extending prior models.

Findings

Identification holds under a mild rank condition.

Model primitives are nonparametrically identified for all signal values.

Framework allows analysis of counterfactual auction scenarios.

Abstract

This paper introduces a version of the interdependent value model of Milgrom and Weber (1982), where the signals are given by an index gathering signal shifters observed by the econometrician and private ones specific to each bidders. The model primitives are shown to be nonparametrically identified from first-price auction bids under a testable mild rank condition. Identification holds for all possible signal values. This allows to consider a wide range of counterfactuals where this is important, as expected revenue in second-price auction. An estimation procedure is briefly discussed.