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http://hdl.handle.net/10419/23554
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| Title: | | Kernel Dependent Functions in Nonparametric Regression with Fractional Time Series Errors  |
| Authors: | | Feng, Yuanhua |
| Issue Date: | | 2003 |
| Series/Report no.: | | Discussion paper series / Universität Konstanz, Center of Finance and Econometrics (CoFE) 03/02 |
| Abstract: | | 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 |
| Subjects: | | Nonparametric regression long memory antipersistence fractional difference kernel dependent function bandwidth selection |
| Document Type: | | Working Paper |
| Appears in Collections: | | CoFE-Diskussionspapiere, Universität Konstanz
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