Publication:
Sparse partial least squares in time series for macroeconomic forecasting

dc.affiliation.dptoUC3M. Departamento de Estadísticaes
dc.contributor.authorFuentes, Julieta
dc.contributor.authorPoncela, Pilar
dc.contributor.authorRodríguez, Julio
dc.contributor.editorUniversidad Carlos III de Madrid. Departamento de Estadística
dc.date.accessioned2012-07-30T11:28:50Z
dc.date.available2012-07-30T11:28:50Z
dc.date.issued2012-08
dc.description.abstractFactor models have been applied extensively for forecasting when high dimensional datasets are available. In this case, the number of variables can be very large. For instance, usual dynamic factor models in central banks handle over 100 variables. However, there is a growing body of the literature that indicates that more variables do not necessarily lead to estimated factors with lower uncertainty or better forecasting results. This paper investigates the usefulness of partial least squares techniques, that take into account the variable to be forecasted when reducing the dimension of the problem from a large number of variables to a smaller number of factors. We propose different approaches of dynamic sparse partial least squares as a means of improving forecast efficiency by simultaneously taking into account the variable forecasted while forming an informative subset of predictors, instead of using all the available ones to extract the factors. We use the well-known Stock and Watson database to check the forecasting performance of our approach. The proposed dynamic sparse models show a good performance in improving the efficiency compared to widely used factor methods in macroeconomic forecasting.
dc.description.sponsorshipPilar Poncela and Julio Rodríguez acknowledge financial support from the Spanish Ministry of Education, contract grant ECO2009-10287
dc.format.mimetypeapplication/pdf
dc.identifier.repecws122216
dc.identifier.urihttps://hdl.handle.net/10016/15026
dc.language.isoeng
dc.relation.ispartofseriesUC3M Working papers. Statistics and Econometrics
dc.relation.ispartofseries12-16
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.otherFactor Models
dc.subject.otherForecasting
dc.subject.otherLarge Datasets
dc.subject.otherPartial Least Squares
dc.subject.otherSparsity
dc.subject.otherVariable Selection
dc.titleSparse partial least squares in time series for macroeconomic forecasting
dc.typeworking paper*
dc.type.hasVersionSMUR*
dspace.entity.typePublication
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