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Out of sample predictability in predictive regressions with many predictor candidates

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dc.affiliation.dptoUC3M. Departamento de Economíaes
dc.contributor.authorGonzalo, Jesús
dc.contributor.authorPitarakis, Jean-Yves
dc.contributor.editorUniversidad Carlos III de Madrid. Departamento de Economíaes
dc.date.accessioned2020-12-09T17:54:55Z
dc.date.available2020-12-09T17:54:55Z
dc.date.issued2020-12-09
dc.description.abstractThis paper is concerned with detecting the presence of out of sample predictability in linear predictive regressions with a potentially large set of candidate predictors. We propose a procedure based on out of sample MSE comparisons that is implementedin a pairwise manner using one predictor at a time and resulting in an aggregate test statistic that is standard normally distributed under the none hypothesis of no linear predictability. Predictors can be highly persistent, purely stationary or a combination of both. Upon rejection of the none hypothesis we subsequently introduce a predictor screening procedure designed to identify the most active predictors.en
dc.identifier.issn2340-5031es
dc.identifier.urihttps://hdl.handle.net/10016/31554
dc.identifier.uxxiDT/0000001856es
dc.language.isoenges
dc.relation.ispartofseriesWorking paper. Economicsen
dc.relation.ispartofseries20-13
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.jelC12
dc.subject.jelC32
dc.subject.jelC52
dc.subject.jelC53
dc.subject.otherForecastingen
dc.subject.otherPredictive Regressionsen
dc.subject.otherHigh Dimensional Predictabilityen
dc.titleOut of sample predictability in predictive regressions with many predictor candidatesen
dc.typeworking paper*
dspace.entity.typePublication
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