Semiparametric Estimation of First-Price Auction Models

55 Pages Posted: 13 Jul 2014 Last revised: 15 Oct 2019

See all articles by Gaurab Aryal

Gaurab Aryal

Washington University in St. Louis

M. Florencia Gabrielli

Universidad del Desarrollo

Quang Vuong

New York University (NYU)

Date Written: August 9, 2016

Abstract

In this paper we propose a two-step semiparametric procedure to estimate first-price auction models. In the first-step, we estimate the bid density and distribution using local polynomial method, and recover a sample of (pseudo) private values. In the second-step, we apply the method of moments to the sample of private values to estimate a finite set of parameters that characterize the density of private values. We show that our estimator attains the parametric consistency rate and is asymptotically normal. And we also determine its asymptotic variance. The advantage of our approach is that it can accommodate multiple auction covariates. Monte Carlo exercises show that the estimator performs well both in estimating the value density and in choosing the revenue maximizing reserve price.

Keywords: Auctions, Structural Approach, Semiparametric Estimator, Local Polynomial, GMM

JEL Classification: C14, C71, D44

Suggested Citation

Aryal, Gaurab and Gabrielli, Maria Florencia and Vuong, Quang, Semiparametric Estimation of First-Price Auction Models (August 9, 2016). Forthcoming, Journal of Business and Economic Statistics, Available at SSRN: https://ssrn.com/abstract=2465394 or http://dx.doi.org/10.2139/ssrn.2465394

Gaurab Aryal

Washington University in St. Louis ( email )

Seigle Hall 335
One Brookings Drive
St. Louis, MO 63130
United States

Maria Florencia Gabrielli (Contact Author)

Universidad del Desarrollo ( email )

School of Business
Ainavillo 456
Concepcion
Chile

Quang Vuong

New York University (NYU) ( email )

Bobst Library, E-resource Acquisitions
20 Cooper Square 3rd Floor
New York, NY 10003-711
United States

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