Publication:
More is not always better : back to the Kalman filter in dynamic factor models

dc.affiliation.dptoUC3M. Departamento de Estadísticaes
dc.contributor.authorPoncela, Pilar
dc.contributor.authorRuiz Ortega, Esther
dc.contributor.editorUniversidad Carlos III de Madrid. Departamento de Estadística
dc.date.accessioned2012-10-29T16:07:34Z
dc.date.available2012-10-29T16:07:34Z
dc.date.issued2012-10
dc.description.abstractIn the context of dynamic factor models (DFM), it is known that, if the cross-sectional and time dimensions tend to infinity, the Kalman filter yields consistent smoothed estimates of the underlying factors. When looking at asymptotic properties, the cross- sectional dimension needs to increase for the filter or stochastic error uncertainty to decrease while the time dimension needs to increase for the parameter uncertainty to decrease. ln this paper, assuming that the model specification is known, we separate the finite sample contribution of each of both uncertainties to the total uncertainty associated with the estimation of the underlying factors. Assuming that the parameters are known, we show that, as far as the serial dependence of the idiosyncratic noises is not very persistent and regardless of whether their contemporaneous correlations are weak or strong, the filter un-certainty is a non-increasing function of the cross-sectional dimension. Furthermore, in situations of empirical interest, if the cross-sectional dimension is beyond a relatively small number, the filter uncertainty only decreases marginally. Assuming weak contemporaneous correlations among the serially uncorrelated idiosyncratic noises, we prove the consistency not only of smooth but also of real time filtered estimates of the underlying factors in a simple case, extending the results to non-stationary DFM. In practice, the model parameters are un-known and have to be estimated, adding further uncertainty to the estimated factors. We use simulations to measure this uncertainty in finite samples and show that, for the sample sizes usually encountered in practice when DFM are fitted to macroeconomic variables, the contribution of the parameter uncertainty can represent a large percentage of the total uncertainty involved in factor extraction. All results are illustrated estimating common factors of simulated time series
dc.description.sponsorshipFinancial support from the Spanish Government projects ECO2009-10287 and ECO2012-32854 is acknowledged by the first author while the second author acknowledges support from projects ECO2009-08100 and ECO2012-32401.
dc.format.mimetypeapplication/pdf
dc.identifier.repecws122317
dc.identifier.urihttps://hdl.handle.net/10016/15782
dc.identifier.uxxiDT/0000000973
dc.language.isoeng
dc.relation.ispartofseriesUC3M Working papers. Statistics and Econometrics
dc.relation.ispartofseries12-17
dc.relation.projectIDGobierno de España. ECO2009-10287
dc.relation.projectIDGobierno de España. ECO2012-32854
dc.relation.projectIDGobierno de España. ECO2009-08100
dc.relation.projectIDGobierno de España. ECO2012-32401
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.ecienciaEstadística
dc.subject.otherCommon factors
dc.subject.otherCross-sectional dimension
dc.subject.otherFilter uncertainty
dc.subject.otherParameter uncertainty
dc.subject.otherSteady-state
dc.titleMore is not always better : back to the Kalman filter in dynamic factor models
dc.typeworking paper*
dc.type.hasVersionSMUR*
dspace.entity.typePublication
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