Please use this identifier to cite or link to this item: https://doi.org/10.4310/SII.2013.v6.n4.a8
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dc.titleA local vector autoregressive framework and its applications to multivariate time series monitoring and forecasting
dc.contributor.authorChen, Y.
dc.contributor.authorLi, B.
dc.contributor.authorNiu, L.
dc.date.accessioned2014-10-28T05:09:08Z
dc.date.available2014-10-28T05:09:08Z
dc.date.issued2013
dc.identifier.citationChen, Y., Li, B., Niu, L. (2013). A local vector autoregressive framework and its applications to multivariate time series monitoring and forecasting. Statistics and its Interface 6 (4) : 499-509. ScholarBank@NUS Repository. https://doi.org/10.4310/SII.2013.v6.n4.a8
dc.identifier.issn19387989
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/104936
dc.description.abstractOur proposed local vector autoregressive (LVAR) model has time-varying parameters that allow it to be safely used in both stationary and non-stationary situations. The estimation is conducted over an interval of local homogeneity where the parameters are approximately constant. The local interval is identified in a sequential testing procedure. Numerical analysis and real data applications are conducted to illustrate the monitoring function and forecast performance of the proposed model.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.4310/SII.2013.v6.n4.a8
dc.sourceScopus
dc.subjectAdaptive estimation
dc.subjectMultivariate time series
dc.subjectNon-stationarity
dc.subjectYield curve
dc.typeArticle
dc.contributor.departmentSTATISTICS & APPLIED PROBABILITY
dc.description.doi10.4310/SII.2013.v6.n4.a8
dc.description.sourcetitleStatistics and its Interface
dc.description.volume6
dc.description.issue4
dc.description.page499-509
dc.identifier.isiut000330487100008
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