Ordinary least squares estimation of a dynamic game model

dc.contributor.authorFABIO ADRIANO MIESSI SANCHES
dc.contributor.authorSilva, Daniel Junior
dc.contributor.authorSrisuma, Sorawoot
dc.coverage.cidadeNão informadopt_BR
dc.coverage.paisEstados Unidospt_BR
dc.creatorSilva, Daniel Junior
dc.creatorSrisuma, Sorawoot
dc.date.accessioned2022-11-11T13:26:54Z
dc.date.available2022-11-11T13:26:54Z
dc.date.issued2016
dc.description.otherEstimation of dynamic games is known to be a numerically challenging task. A common form of the payoff functions employed in practice takes the linear-in-parameter specification. We show a least squares estimator taking a familiar OLS/GLS expression is available in such a case. Our proposed estimator has a closed form. It can be computed without any numerical optimization and always minimizes the least squares objective function.We specify the optimally weighted GLS estimator that is efficient in the class of estimators under consideration. Our estimator appears to perform well in a simple Monte Carlo experiment.pt_BR
dc.format.extentp. 623-623pt_BR
dc.format.mediumDigitalpt_BR
dc.identifier.issue2pt_BR
dc.identifier.urihttps://repositorio.insper.edu.br/handle/11224/4704
dc.identifier.volume57pt_BR
dc.language.isoInglêspt_BR
dc.publisherEconomics Department of the University of Pennsylvaniapt_BR
dc.publisherOsaka University Institute of Social and Economic Research Associationpt_BR
dc.relation.ispartofInternational Economic Reviewpt_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.keywordsNão informadopt_BR
dc.titleOrdinary least squares estimation of a dynamic game modelpt_BR
dc.typejournal article
dspace.entity.typePublication
local.subject.cnpqCiências Sociais Aplicadaspt_BR
local.typeArtigo Científicopt_BR
relation.isAuthorOfPublication80b08d65-f22c-4df9-9e17-9b169f92ceed
relation.isAuthorOfPublication.latestForDiscovery80b08d65-f22c-4df9-9e17-9b169f92ceed
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