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dc.contributor.authorHarvey, Andrew C.
dc.contributor.authorDe Rossi, Giuliano
dc.date.accessioned2006-07-25T09:34:35Z
dc.date.available2006-07-25T09:34:35Z
dc.date.issued2006-07
dc.identifier.otherCWPE0649
dc.identifier.urihttp://www.dspace.cam.ac.uk/handle/1810/183636
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/183636
dc.description.abstractA time-varying quantile can be fitted to a sequence of observations by formulating a time series model for the corresponding population quantile and iteratively applying a suitably modified state space signal extraction algorithm. Quantiles estimated in this way provide information on various aspects of a time series, including dispersion, asymmetry and, for financial applications, value at risk. Tests for the constancy of quantiles, and associated contrasts, are constructed using indicator variables; these tests have a similar form to stationarity tests and, under the null hypothesis, their asymptotic distributions belong to the Cramér von Mises family. Estimates of the quantiles at the end of the series provide the basis for forecasting. As such they offer an alternative to conditional quantile autoregressions and, at the same time, give some insight into their structure and potential drawbacks.en
dc.format.extent428890 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherFaculty of Economics
dc.relation.ispartofseriesCambridge Working Papers in Economics
dc.rightsAll Rights Reserveden
dc.rights.urihttps://www.rioxx.net/licenses/all-rights-reserved/en
dc.subjectDispersionen
dc.subjectquantile regressionen
dc.subjectsignal extractionen
dc.subjectstate spaceen
dc.subjectstationarity testsen
dc.subjectvalue at risken
dc.titleTime-Varying Quantilesen
dc.typeWorking Paperen
dc.identifier.doi10.17863/CAM.5455


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