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Smoothed Conditional Scale Function Estimation in AR(1)-ARCH(1) Processes

dc.contributor.authorSeknewna, Lema L.
dc.contributor.authorMwita, Peter N.
dc.contributor.authorMuema, B.
dc.date.accessioned2018-11-20T11:46:27Z
dc.date.available2018-11-20T11:46:27Z
dc.date.issued2018-03-12
dc.description.abstractThe estimation of the Smoothed Conditional Scale Function for time series was taken out under the conditional heteroscedastic innovations by imitating the kernel smoothing in nonparametric QAR-QARCH scheme. The estimation was taken out based on the quantile regression methodology proposed by Koenker and Bassett. And the proof of the asymptotic properties of the Conditional Scale Function estimator for this type of process was given and its consistency was shown.
dc.identifier.urihttp://ir.mksu.ac.ke/handle/123456780/1895
dc.language.isoen_USen_US
dc.publisherHindawi Publishing Corporationen_US
dc.titleSmoothed Conditional Scale Function Estimation in AR(1)-ARCH(1) Processesen_US
dc.typeArticleen_US

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