Sparse partial least squares in time series for macroeconomic forecasting

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dc.contributor.author Fuentes, Julieta
dc.contributor.author Poncela, Pilar
dc.contributor.author Rodríguez, Julio
dc.contributor.editor Universidad Carlos III de Madrid. Departamento de Estadística
dc.date.accessioned 2012-07-30T11:28:50Z
dc.date.available 2012-07-30T11:28:50Z
dc.date.issued 2012-08
dc.identifier.uri http://hdl.handle.net/10016/15026
dc.description.abstract Factor 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.sponsorship Pilar Poncela and Julio Rodríguez acknowledge financial support from the Spanish Ministry of Education, contract grant ECO2009-10287
dc.format.mimetype application/pdf
dc.language.iso eng
dc.relation.ispartofseries UC3M Working papers. Statistics and Econometrics
dc.relation.ispartofseries 12-16
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other Factor Models
dc.subject.other Forecasting
dc.subject.other Large Datasets
dc.subject.other Partial Least Squares
dc.subject.other Sparsity
dc.subject.other Variable Selection
dc.title Sparse partial least squares in time series for macroeconomic forecasting
dc.type workingPaper
dc.subject.eciencia Estadística
dc.rights.accessRights openAccess
dc.type.version submitedVersion
dc.identifier.repec ws122216
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