Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/56710
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dc.contributor.authorHärdle, Wolfgang Karlen_US
dc.contributor.authorSpokoiny, Vladimiren_US
dc.contributor.authorWang, Weiningen_US
dc.date.accessioned2011-01-05en_US
dc.date.accessioned2012-04-05T16:19:45Z-
dc.date.available2012-04-05T16:19:45Z-
dc.date.issued2010en_US
dc.identifier.urihttp://hdl.handle.net/10419/56710-
dc.description.abstractConditional quantile curves provide a comprehensive picture of a response contingent on explanatory variables. Quantile regression is a technique to estimate such curves. In a flexible modeling framework, a specific form of the quantile is not a priori fixed. Indeed, the majority of applications do not per se require specific functional forms. This motivates a local parametric rather than a global fixed model fitting approach. A nonparametric smoothing estimate of the conditional quantile curve requires to consider a balance between local curvature and variance. In this paper, we analyze a method based on a local model selection technique that provides an adaptive estimate. Theoretical properties on mimicking the oracle choice are offered and applications to stock market and weather analysis are presented.en_US
dc.language.isoengen_US
dc.publisher|aSFB 649, Economic Risk |cBerlinen_US
dc.relation.ispartofseries|aSFB 649 discussion paper |x2011-005en_US
dc.subject.jelC00en_US
dc.subject.jelC14en_US
dc.subject.jelJ01en_US
dc.subject.jelJ31en_US
dc.subject.ddc330en_US
dc.subject.keywordconditional quantilesen_US
dc.subject.keywordsemiparametric and nonparametric methodsen_US
dc.subject.keywordasymmetric Laplace distributionen_US
dc.subject.keywordexponential risk boundsen_US
dc.subject.keywordadaptive bandwidth selectionen_US
dc.subject.stwRegressionen_US
dc.subject.stwNichtparametrisches Verfahrenen_US
dc.subject.stwTheorieen_US
dc.titleLocal quantile regressionen_US
dc.typeWorking Paperen_US
dc.identifier.ppn642769036en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen_US

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