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Bootstrap of Kernel Smoothing in Quantile Autoregression Process

dc.contributor.authorMwita, Peter N.
dc.contributor.authorFranke, J¨urgen
dc.date.accessioned2018-11-15T07:17:14Z
dc.date.available2018-11-15T07:17:14Z
dc.date.issued2013
dc.description.abstractThe paper considers the problem of bootstrapping kernel estimator of conditional quantiles for time series, under independent and identically distributed errors, by mimicking the kernel smoothing in nonparametric autoregressive scheme. A quantile autoregression bootstrap generating process is constructed and the estimator given. Under appropriate assumptions, the bootstrap estimator is shown to be consistent.en_US
dc.identifier.issn1792-6602 (print)
dc.identifier.issn1792-6939 (online)
dc.identifier.urihttp://ir.mksu.ac.ke/handle/123456780/1523
dc.language.isoen_USen_US
dc.publisherJournal of Statistical and Econometric Methodsen_US
dc.subjectAsymptoticen_US
dc.subjectBootstrapen_US
dc.subjectQuantile Autoregressionen_US
dc.titleBootstrap of Kernel Smoothing in Quantile Autoregression Processen_US
dc.typeArticleen_US

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