On aggregation of strongly dependent time series
| Type of Publication: | Journal article |
| Publication status: | Published |
| Author: | Beran, Jan; Liu, Haiyan; Ghosh, Sucharita |
| Year of publication: | 2019 |
| Published in: | Scandinavian Journal of Statistics ; 2019. - Wiley. - ISSN 0303-6898. - eISSN 1467-9469 |
| DOI (citable link): | https://dx.doi.org/10.1111/sjos.12421 |
| Summary: |
We consider cross‐sectional aggregation of time series with long‐range dependence. This question arises for instance from the statistical analysis of networks where aggregation is defined via routing matrices. Asymptotically, aggregation turns out to increase dependence substantially, transforming a hyperbolic decay of autocorrelations to a slowly varying rate. This effect has direct consequences for statistical inference. For instance, unusually slow rates of convergence for nonparametric trend estimators and nonstandard formulas for optimal bandwidths are obtained. The situation changes, when time‐dependent aggregation is applied. Suitably chosen time‐dependent aggregation schemes can preserve a hyperbolic rate or even eliminate autocorrelations completely.
|
| Subject (DDC): | 510 Mathematics |
| Refereed: | Yes |
| Online First: Journal articles that are published online before they appear as an actual part of a journal issue. Online first articles are published on the journal's website in the publisher's version. | |
Cite This
Files in this item
| Files | Size | Format | View |
|---|---|---|---|
|
There are no files associated with this item. |
|||
BERAN, Jan, Haiyan LIU, Sucharita GHOSH, 2019. On aggregation of strongly dependent time series. In: Scandinavian Journal of Statistics. Wiley. ISSN 0303-6898. eISSN 1467-9469. Available under: doi: 10.1111/sjos.12421
@article{Beran2019-12-13aggre-48631, title={On aggregation of strongly dependent time series}, year={2019}, doi={10.1111/sjos.12421}, issn={0303-6898}, journal={Scandinavian Journal of Statistics}, author={Beran, Jan and Liu, Haiyan and Ghosh, Sucharita} }
<rdf:RDF xmlns:dcterms="http://purl.org/dc/terms/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bibo="http://purl.org/ontology/bibo/" xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#" xmlns:foaf="http://xmlns.com/foaf/0.1/" xmlns:void="http://rdfs.org/ns/void#" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > <rdf:Description rdf:about="https://kops.uni-konstanz.de/rdf/resource/123456789/48631"> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2020-02-13T12:25:08Z</dcterms:available> <dc:contributor>Beran, Jan</dc:contributor> <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/rdf/resource/123456789/39"/> <dcterms:issued>2019-12-13</dcterms:issued> <dc:contributor>Liu, Haiyan</dc:contributor> <foaf:homepage rdf:resource="http://localhost:8080/jspui"/> <dc:language>eng</dc:language> <dcterms:title>On aggregation of strongly dependent time series</dcterms:title> <dc:creator>Liu, Haiyan</dc:creator> <dc:creator>Beran, Jan</dc:creator> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2020-02-13T12:25:08Z</dc:date> <dc:contributor>Ghosh, Sucharita</dc:contributor> <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/48631"/> <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/rdf/resource/123456789/39"/> <dc:creator>Ghosh, Sucharita</dc:creator> <dcterms:abstract xml:lang="eng">We consider cross‐sectional aggregation of time series with long‐range dependence. This question arises for instance from the statistical analysis of networks where aggregation is defined via routing matrices. Asymptotically, aggregation turns out to increase dependence substantially, transforming a hyperbolic decay of autocorrelations to a slowly varying rate. This effect has direct consequences for statistical inference. For instance, unusually slow rates of convergence for nonparametric trend estimators and nonstandard formulas for optimal bandwidths are obtained. The situation changes, when time‐dependent aggregation is applied. Suitably chosen time‐dependent aggregation schemes can preserve a hyperbolic rate or even eliminate autocorrelations completely.</dcterms:abstract> </rdf:Description> </rdf:RDF>
This item appears in the following Collection(s)
Search KOPS
Browse
My Account
