A robust procedure to build dynamic factor models with cluster structure

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dc.contributor.author Alonso Fernández, Andrés Modesto
dc.contributor.author Galeano San Miguel, Pedro
dc.contributor.author Peña, Daniel
dc.date.accessioned 2021-06-28T07:41:29Z
dc.date.available 2022-05-01T23:00:06Z
dc.date.issued 2020-05
dc.identifier.bibliographicCitation Alonso, A. M., Galeano, P. & Peña, D. (2020). A robust procedure to build dynamic factor models with cluster structure. Journal of Econometrics, 216(1), pp. 35–52.
dc.identifier.issn 0304-4076
dc.identifier.uri http://hdl.handle.net/10016/32939
dc.description.abstract Dynamic factor models provide a useful way to model large sets of time series. These data often have heterogeneity and cluster structure and the formulation and estimation of dynamic factor models should be adapted to these features. This article presents a procedure to fit Dynamic Factor Models with Cluster Structure (DFMCS), where some of the factors are global and others group-specific, to heterogeneous data that may include multivariate additive outliers and level shifts. The procedure starts with an initial cleaning of the times series from outlying effects. Then a first estimation of the possible factors is applied to the cleaned data and these factors are used to build the common component of each series. The groups are found by studying the joint dependency of these common components. Then, additional factors are estimated by using the series in each cluster and, finally, all the factors found are classified as global or group-specific. We show in a Monte Carlo study that the procedure works well and seems to be better than other alternatives in terms of estimation of factors and loadings as well as in terms of misclassification rates for the series. An example of an electricity market is presented to illustrate the advantages of cleaning for outliers and taking into account the cluster structure for understanding and forecasting.
dc.format.extent 18
dc.language.iso eng
dc.publisher Elsevier
dc.rights © 2020 Elsevier B.V.
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other Clustering time series
dc.subject.other Dependency measures
dc.subject.other Multivariate additive outliers
dc.subject.other Multivariate level shifts
dc.subject.other Principal components
dc.title A robust procedure to build dynamic factor models with cluster structure
dc.type article
dc.subject.eciencia Estadística
dc.identifier.doi https://doi.org/10.1016/j.jeconom.2020.01.004
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. ECO2015-66593-P
dc.type.version acceptedVersion
dc.identifier.publicationfirstpage 35
dc.identifier.publicationissue 1
dc.identifier.publicationlastpage 52
dc.identifier.publicationtitle Journal of Econometrics
dc.identifier.publicationvolume 216
dc.identifier.uxxi AR/0000027642
dc.contributor.funder Ministerio de Economía y Competitividad (España)
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