A Parametric Estimation Method for Dynamic Factor Models of Large Dimensions

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dc.contributor.author KAPETANIOS, George
dc.contributor.author MARCELLINO, Massimiliano
dc.date.accessioned 2010-12-10T14:22:39Z
dc.date.available 2010-12-10T14:22:39Z
dc.date.issued 2009-01-01
dc.identifier.citation Journal of Time Series Analysis, 2009, 30, 2, 208-238 en
dc.identifier.issn 1467-9892
dc.identifier.issn 0143-9782
dc.identifier.uri http://hdl.handle.net/1814/15180
dc.description.abstract The 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.iso en en
dc.title A Parametric Estimation Method for Dynamic Factor Models of Large Dimensions en
dc.type Article en
dc.neeo.contributor KAPETANIOS|George|aut|
dc.neeo.contributor MARCELLINO|Massimiliano|aut|EUI70008


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