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dc.contributor.author
Aryal, Gaurab
dc.contributor.author
Gabrielli, Maria Florencia
dc.contributor.author
Vuong, Quang
dc.date.available
2022-11-01T12:27:57Z
dc.date.issued
2019-10
dc.identifier.citation
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
dc.identifier.issn
0735-0015
dc.identifier.uri
http://hdl.handle.net/11336/175781
dc.description.abstract
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.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
American Statistical Association
dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
EMPIRICAL AUCTIONS
dc.subject
GMM
dc.subject
LOCAL POLYNOMIAL
dc.subject
SEMIPARAMETRIC ESTIMATOR
dc.subject.classification
Economía, Econometría
dc.subject.classification
Economía y Negocios
dc.subject.classification
CIENCIAS SOCIALES
dc.title
Semiparametric estimation of first-price auction models
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2022-10-25T14:41:59Z
dc.journal.volume
39
dc.journal.number
2
dc.journal.pagination
1-14
dc.journal.pais
Estados Unidos
dc.journal.ciudad
Boston
dc.description.fil
Fil: Aryal, Gaurab. University of Virginia; Estados Unidos
dc.description.fil
Fil: Gabrielli, Maria Florencia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Cuyo. Facultad de Ciencias Económicas. Centro de Investigación Cuyo; Argentina
dc.description.fil
Fil: Vuong, Quang. University of New York; Estados Unidos
dc.journal.title
Journal Of Business & Economic Statistics
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.tandfonline.com/doi/full/10.1080/07350015.2019.1665530
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1080/07350015.2019.1665530
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