Out of sample predictability in predictive regressions with many predictor candidates

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dc.contributor.author Gonzalo, Jesús
dc.contributor.author Pitarakis, Jean-Yves
dc.contributor.editor Universidad Carlos III de Madrid. Departamento de Economía
dc.date.accessioned 2020-12-09T17:54:55Z
dc.date.available 2020-12-09T17:54:55Z
dc.date.issued 2020-12-09
dc.identifier.issn 2340-5031
dc.identifier.uri http://hdl.handle.net/10016/31554
dc.description.abstract This 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.
dc.language.iso eng
dc.relation.ispartofseries Working paper. Economics
dc.relation.ispartofseries 20-13
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other Forecasting
dc.subject.other Predictive Regressions
dc.subject.other High Dimensional Predictability
dc.title Out of sample predictability in predictive regressions with many predictor candidates
dc.type workingPaper
dc.subject.jel C12
dc.subject.jel C32
dc.subject.jel C52
dc.subject.jel C53
dc.identifier.uxxi DT/0000001856
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