Data-driven estimation of semiparametric fractional autoregressive models
In this paper data-driven algorithms for fitting SEMIFAR models (Beran, 1999) are proposed. The algorithms combine the data-driven estimation of the nonparametric trend and maximum likelihood estimation of the parameters. For selecting the bandwidth, the proposal of Beran and Feng (1999) based on the iterative plug-in idea (Gasser et al., 1991) is used. Asymptotic properties of the proposed algorithms are investigated. A large simulation study illustrates the practical performance of the methods.
Year of publication: |
2000-06
|
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Authors: | Beran, Jan ; Feng, Yuanhua |
Institutions: | Zentrum für Finanzen und Ökonometrie, Fachbereich Wirtschaftswissenschaften |
Saved in:
freely available
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