Minimum distance estimation of search costs using price distribution

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dc.contributor.authorFABIO ADRIANO MIESSI SANCHES
dc.contributor.authorSilva Junior, Daniel
dc.contributor.authorSrisuma, Sorawoot
dc.coverage.paisNão Informadopt_BR
dc.creatorSilva Junior, Daniel
dc.creatorSrisuma, Sorawoot
dc.date.accessioned2022-11-11T14:23:24Z
dc.date.available2022-11-11T14:23:24Z
dc.date.issued2018
dc.description.otherIt has been shown that equilibrium restrictions in a search model can be used to identify quantiles of the search cost distribution from observedprices alone. These quantiles can be difficult to estimate in practice. This article uses a minimum distance approach to estimate them that is easy to compute. A version of our estimator is a solution to a nonlinear least-square problem that can be straightforwardly programmed on softwares such as STATA. We show our estimator is consistent and has an asymptotic normal distribution. Its distribution can be consistently estimated by a bootstrap. Our estimator can be used to estimate the cost distribution nonparametrically on a larger support when prices from heterogenous markets are available.We propose a two-step sieve estimator for that case. The first step estimates quantiles from each market. They are used in the second step as generated variables to perform nonparametric sieve estimation. We derive the uniform rate of convergence of the sieve estimator that can be used to quantify the errors incurred from interpolating data across markets. To illustrate we use online bookmaking odds for English football leagues’ matches (as prices) and find evidence that suggests search costs for consumers have fallen following a change in the British law that allows gambling operators to advertise more widely. Supplementary materials for this article are available online.pt_BR
dc.format.extentp. 658-671pt_BR
dc.format.mediumDigitalpt_BR
dc.identifier.doi10.1080/07350015.2016.1247003pt_BR
dc.identifier.issn0735-0015pt_BR
dc.identifier.issn1537-2707pt_BR
dc.identifier.issue4pt_BR
dc.identifier.urihttps://repositorio.insper.edu.br/handle/11224/4710
dc.identifier.volume36pt_BR
dc.language.isoInglêspt_BR
dc.publisherTaylor & Francis Grouppt_BR
dc.relation.ispartofJournal of Business & Economic Statisticspt_BR
dc.rights.licenseO INSPER E ESTE REPOSITÓRIO NÃO DETÊM OS DIREITOS DE USO E REPRODUÇÃO DOS CONTEÚDOS AQUI REGISTRADOS. É RESPONSABILIDADE DOS USUÁRIOS INDIVIDUAIS VERIFICAR OS USOS PERMITIDOS NA FONTE ORIGINAL, RESPEITANDO-SE OS DIREITOS DE AUTOR OU EDITOR.pt_BR
dc.subject.keywordsBootstrappt_BR
dc.subject.keywordsGenerated variablespt_BR
dc.subject.keywordsM-Estimationpt_BR
dc.subject.keywordsSearch costpt_BR
dc.subject.keywordsSieve estimationpt_BR
dc.titleMinimum distance estimation of search costs using price distributionpt_BR
dc.typejournal article
dspace.entity.typePublication
local.identifier.sourceUrihttps://doi.org/10.1080/07350015.2016.1247003
local.typeArtigo Científicopt_BR
relation.isAuthorOfPublication80b08d65-f22c-4df9-9e17-9b169f92ceed
relation.isAuthorOfPublication.latestForDiscovery80b08d65-f22c-4df9-9e17-9b169f92ceed
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