Semiparametric Estimation of First-Price Auction Models

TL;DR

This paper introduces a semiparametric estimation method for private values in first-price auctions, combining local polynomial estimation with GMM, achieving consistency and root-n convergence.

Contribution

It develops a novel two-step semiparametric approach for auction models, integrating local polynomial and GMM techniques for improved estimation.

Findings

Estimator is consistent and asymptotically normal

Achieves parametric convergence rate

Validates method through theoretical analysis

Abstract

We propose a semiparametric method to estimate the density of private values in first-price auctions. Specifically, we model private values through a set of conditional moment restrictions and use a two-step procedure. In the first step we recover a sample of pseudo private values using Local Polynomial Estimator. In the second step we use a GMM procedure to estimate the parameter(s) of interest. We show that the proposed semiparametric estimator is consistent, has an asymptotic normal distribution, and attains the parametric ("root-n") rate of convergence.