Bootstrap methods for generalized autoregressive moving average models

dc.contributorGlaura da Conceicao Franco
dc.contributorIvair Ramos Silva
dc.contributorMarcos Oliveira Prates
dc.creatorMatheus de Vasconcellos Barroso
dc.date.accessioned2019-08-11T14:51:56Z
dc.date.accessioned2022-10-03T23:47:01Z
dc.date.available2019-08-11T14:51:56Z
dc.date.available2022-10-03T23:47:01Z
dc.date.created2019-08-11T14:51:56Z
dc.date.issued2018-06-11
dc.identifierhttp://hdl.handle.net/1843/BIRC-BB4NX5
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/3827991
dc.description.abstractThis final paper aims to find a suitable Bootstrap Method for the Generalized Autoregressive Moving Average Model. The focus is on the Moving Block Bootstrap (MBB) resampling scheme with its performance being evaluated through a Monte Carlo study and contrasted to their asymptotic Gaussian counterpart. It is stablished that the aforementioned resampling procedure can generate good estimates of parameters bias and confidence intervals. Though, the results rely heavily on the simulated model parameters and block lengths used in the MBB procedure.
dc.publisherUniversidade Federal de Minas Gerais
dc.publisherUFMG
dc.rightsAcesso Aberto
dc.subjectMBB
dc.subjectTime- Series BOOTSTRAP
dc.subjectGARMA models
dc.titleBootstrap methods for generalized autoregressive moving average models
dc.typeDissertação de Mestrado


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