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Activity Number:
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15
- Networks, Multivariate Analysis, and Time Series
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Type:
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Topic Contributed
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Date/Time:
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Sunday, July 30, 2017 : 2:00 PM to 3:50 PM
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Sponsor:
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Royal Statistical Society
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Abstract #323327
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Title:
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Semiparametric, Parametric and Possibly Sparse Models for Multivariate Long-Range Dependence
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Author(s):
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Vladas Pipiras* and Stefanos Kechagias and Changryong Baek
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Companies:
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University Od North Carolina At Chaple Hill and SAS Institute and Sungkyunkwan University
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Keywords:
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vector time series ;
long-range dependence ;
phase parameters ;
parametric model ;
sparsity
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Abstract:
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The focus of this talk is on multivariate (vector-valued) time series that exhibit long-range dependence (LRD) and, more specifically, on (semi-)parametric models that account for general phase parameters in the cross spectra of the series at the zero frequency. Several new multivariate LRD time series models are introduced and their estimation is discussed, possibly assuming sparsity of model parameters. Applications to several real time series are also presented.
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Authors who are presenting talks have a * after their name.
