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Kernel dependent functions in nonparametric regression with fractional time series errors

Kernel dependent functions in nonparametric regression with fractional time series errors

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Prüfsumme: MD5:4ceaa02eca02a97cc68153ebdc69f15e

FENG, Yuanhua, 2003. Kernel dependent functions in nonparametric regression with fractional time series errors

@techreport{Feng2003Kerne-11969, series={CoFE-Diskussionspapiere / Zentrum für Finanzen und Ökonometrie}, title={Kernel dependent functions in nonparametric regression with fractional time series errors}, year={2003}, number={2003/02}, author={Feng, Yuanhua} }

This paper considers estimation of the regression function and its derivatives in nonparametric regression with fractional time series errors. We focus on investigating the properties of a kernel dependent function V (delta) in the asymptotic variance and finding closed form formula of it, where delta is the long-memory parameter. - General solution of V (delta) for polynomial kernels is given together with a few examples. It is also found, e.g. that the Uniform kernel is no longer the minimum variance one by strongly antipersistent errors and that, for a fourth order kernel, V (delta) at some delta > 0 is clearly smaller than R(K). The results are used to develop a general data-driven algorithm. Data examples illustrate the practical relevance of the approach and the performance of the algorithm. eng 2003 Kernel dependent functions in nonparametric regression with fractional time series errors 2011-03-25T09:41:27Z deposit-license 2011-03-25T09:41:27Z Feng, Yuanhua application/pdf Feng, Yuanhua

Dateiabrufe seit 01.10.2014 (Informationen über die Zugriffsstatistik)

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