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dc.contributor.authorSanches, Nathalie C. Gimenes Miessi copyright of this thesis rests with the author and no quotation from it or information derived from it may be published without the prior written consent of the author
dc.identifier.citationSanches, Nathalie C.M. 2014. Quantile Regression Approaches for Auctions. Queen Mary University of Londonen_US
dc.description.abstractThe goal of this thesis is to propose a new quantile regression approach to identify and estimate the quantiles of the private value conditional distribution in ascending and rst price auctions under the Independent Private Value (IPV) paradigm. The quantile regression framework provides a exible and convenient parametrization of the private value distribution, which is not a ected by the curse of dimensionality. The rst Chapter of the thesis introduces a quantile regression methodology for ascending auctions. The Chapter focuses on revenue analysis, optimal reservation price and its associated screening level. An empirical application for the USFS timber auctions suggests an optimal reservation price policy with a probability of selling the good as low as 58% for some auctions with two bidders. The second Chapter tries to address this issue by considering a risk averse seller with a CRRA utility function. A numerical exercise based on the USFS timber auctions shows that increasing the CRRA of the sellers is su cient to give more reasonable policy recommendations and a higher probability of selling the auctioned timber lot. The third Chapter develops a quantile regression methodology for rst-price auction. The estimation method combines local polynomial, quantile regression and additive sieve methods. It is shown in addition that the new quantile regression methodology is not subject to boundary issues. The choice of smoothing parameters is also discussed.en_US
dc.description.sponsorshipSchool of Economics and Finance at Queen Mary, University of Londen_US
dc.publisherQueen Mary University of Londonen_US
dc.subjectquantile regressionen_US
dc.titleQuantile Regression Approaches for Auctionsen_US

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    Theses Awarded by Queen Mary University of London

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