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Factor extraction using Kalman filter and smoothing: this is not just another survey

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dc.affiliation.dptoUC3M. Departamento de Estadísticaes
dc.contributor.authorPoncela Blanco, Maria Pilar
dc.contributor.authorRuiz Ortega, Esther
dc.contributor.authorMiranda Gualdrón, Karen Alejandra
dc.contributor.editorUniversidad Carlos III de Madrid. Departamento de Estadísticaes
dc.date.accessioned2020-06-25T17:08:57Z
dc.date.available2020-06-25T17:08:57Z
dc.date.issued2020-06-25
dc.description.abstractDynamic Factor Models, which assume the existence of a small number of unobservedlatent factors that capture the comovements in a system of variables, are the main "bigdata" tool used by empirical macroeconomists during the last 30 years. One importanttool to extract the factors is based on Kalman lter and smoothing procedures that cancope with missing data, mixed frequency data, time-varying parameters, non-linearities,non-stationarity and many other characteristics often observed in real systems of economicvariables. This paper surveys the literature on latent common factors extracted using Kalmanfilter and smoothing procedures in the context of Dynamic Factor Models. Signal extractionand parameter estimation issues are separately analyzed. Identi cation issues are also tackledin both stationary and non-stationary models. Finally, empirical applications are surveyedin both cases.en
dc.identifier.issn2387-0303
dc.identifier.urihttps://hdl.handle.net/10016/30644
dc.identifier.uxxiDT/0000001767es
dc.language.isoenges
dc.relation.ispartofseriesWorking paper. Statistics and Econometricses
dc.relation.ispartofseries20-05es
dc.relation.projectIDGobierno de España. ECO2015-70331-C2-2-Res
dc.relation.projectIDGobierno de España. ECO2015-70331-C2-1-Res
dc.relation.projectIDGobierno de España. PID2019-108079GBC22es
dc.relation.projectIDGobierno de España. PID2019-108079GB-C21es
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.otherDynamic Factor Modelen
dc.subject.otherEm Algorithmen
dc.subject.otherIdenti Cationen
dc.subject.otherState-Space Modelen
dc.titleFactor extraction using Kalman filter and smoothing: this is not just another surveyen
dc.typeworking paper*
dspace.entity.typePublication
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