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dc.contributor.authorKAPETANIOS, George
dc.contributor.authorMARCELLINO, Massimiliano
dc.date.accessioned2010-12-10T14:22:39Z
dc.date.available2010-12-10T14:22:39Z
dc.date.issued2009-01-01
dc.identifier.citationJournal of Time Series Analysis, 2009, 30, 2, 208-238en
dc.identifier.issn1467-9892
dc.identifier.issn0143-9782
dc.identifier.urihttp://hdl.handle.net/1814/15180
dc.description.abstractThe estimation of dynamic factor models for large sets of variables has attracted considerable attention recently, because of the increased availability of large data sets. In this article we propose a new parametric methodology for estimating factors from large data sets based on state–space models and discuss its theoretical properties. In particular, we show that it is possible to estimate consistently the factor space. We also conduct a set of simulation experiments that show that our approach compares well with existing alternatives.en
dc.language.isoenen
dc.titleA Parametric Estimation Method for Dynamic Factor Models of Large Dimensionsen
dc.typeArticleen
dc.neeo.contributorKAPETANIOS|George|aut|
dc.neeo.contributorMARCELLINO|Massimiliano|aut|EUI70008


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