Artículo
Semiparametric estimation of first-price auction models
Fecha de publicación:
10/2019
Editorial:
American Statistical Association
Revista:
Journal Of Business & Economic Statistics
ISSN:
0735-0015
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
In this article, 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. Supplementary materials for this article are available online.
Palabras clave:
EMPIRICAL AUCTIONS
,
GMM
,
LOCAL POLYNOMIAL
,
SEMIPARAMETRIC ESTIMATOR
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Articulos(CCT - MENDOZA)
Articulos de CTRO.CIENTIFICO TECNOL.CONICET - MENDOZA
Articulos de CTRO.CIENTIFICO TECNOL.CONICET - MENDOZA
Citación
Aryal, Gaurab; Gabrielli, Maria Florencia; Vuong, Quang; Semiparametric estimation of first-price auction models; American Statistical Association; Journal Of Business & Economic Statistics; 39; 2; 10-2019; 1-14
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