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Pooling information and forecasting with dynamic factor analysis

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dc.affiliation.dptoUC3M. Departamento de Estadísticaes
dc.contributor.authorPeña, Daniel
dc.contributor.authorPoncela, Pilar
dc.contributor.editorUniversidad Carlos III de Madrid. Departamento de Estadísticaes
dc.date.accessioned2011-04-11T16:43:37Z
dc.date.available2011-04-11T16:43:37Z
dc.date.issued1996-11
dc.description.abstractIn this paper, we present a generalized dynamic factor model for a vector of time series, which seems to provide a general framework to incorporate all the common information included in a collection of variables. The common dynamic structure is explained through a set of common factors, which may be stationary, or nonstationary as in the case of common trends. Also, it may exist a specific structure for each variable. Identification of the non stationary factors is made through the common eigenstructure of the lagged co variance matrices. Estimation of the model is carried out in state space form with the EM algorithm, where the Kalman filter is used to estimate the factors or not observable variables. It is shown that this approach implies, as particular cases, many pooled forecasting procedures suggested in the literature. In particular, it offers an explanation to the empirical fact that the forecasting performance of a time series vector is improved when the overall mean is incorporated into the forecast equation for each component.es
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/10016/10709
dc.language.isoenges
dc.relation.ispartofseriesUC3M Working papers. Statistics and Econometricses
dc.relation.ispartofseries96-63es
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.ecienciaEstadísticaes
dc.subject.otherCo integration and common factorses
dc.subject.otherGeneralized factor modeles
dc.subject.otherKalman filteres
dc.subject.otherPooling techniqueses
dc.subject.otherVector time serieses
dc.titlePooling information and forecasting with dynamic factor analysises
dc.typeworking paper*
dspace.entity.typePublication
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