Bootstrap of Kernel Smoothing in Quantile Autoregression Process

dc.contributor.author	Mwita, Peter N.
dc.contributor.author	Franke, J¨urgen
dc.date.accessioned	2018-11-15T07:17:14Z
dc.date.available	2018-11-15T07:17:14Z
dc.date.issued	2013
dc.identifier.issn	1792-6602 (print)
dc.identifier.issn	1792-6939 (online)
dc.identifier.uri	http://ir.mksu.ac.ke/handle/123456780/1523
dc.description.abstract	The 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.language.iso	en_US	en_US
dc.publisher	Journal of Statistical and Econometric Methods	en_US
dc.subject	Asymptotic	en_US
dc.subject	Bootstrap	en_US
dc.subject	Quantile Autoregression	en_US
dc.title	Bootstrap of Kernel Smoothing in Quantile Autoregression Process	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