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

dc.contributor.author	Seknewna, Lema L.
dc.contributor.author	Mwita, Peter N.
dc.contributor.author	Muema, B.
dc.date.accessioned	2018-11-20T11:46:27Z
dc.date.available	2018-11-20T11:46:27Z
dc.date.issued	2018-03-12
dc.identifier.uri	http://ir.mksu.ac.ke/handle/123456780/1895
dc.description.abstract	The 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.language.iso	en_US	en_US
dc.publisher	Hindawi Publishing Corporation	en_US
dc.title	Smoothed Conditional Scale Function Estimation in AR(1)-ARCH(1) Processes	en_US
dc.type	Article	en_US

School of Pure and Applied Sciences [259]
Scholarly Articles by Faculty & Students in the School of Pure and Applied Sciences