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Robust filtering with quantile regression

dc.contributor.authorAssunção, João Borges
dc.contributor.authorFernandes, Pedro Afonso
dc.date.accessioned2022-07-21T14:18:36Z
dc.date.available2022-07-21T14:18:36Z
dc.date.issued2022-02-21
dc.description.abstractThis working paper proposes a new, practical method to compute the non-linear Mosheiov-Raveh (MR) filter using least absolute deviations (LAD) instead of the linear programming approach proposed by these two authors. This paper is embodied with an implementation in the R programming language of the proposed method which facilitates the computation of the MR filter in current applications to produce a robust estimate, namely, of the GDP trend growth. This technique may be appropriate to deal with non linear time series or structural changes.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.urihttp://hdl.handle.net/10400.14/38332
dc.language.isoengpt_PT
dc.subjectBusiness cyclespt_PT
dc.subjectNon linear time seriespt_PT
dc.subjectRobust filteringpt_PT
dc.subjectSoftwarept_PT
dc.titleRobust filtering with quantile regressionpt_PT
dc.typeworking paper
dspace.entity.typePublication
oaire.citation.titleRobust filtering with quantile regressionpt_PT
rcaap.rightsopenAccesspt_PT
rcaap.typeworkingPaperpt_PT

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