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dc.contributor.authorHärdle, Wolfgang Karlen_US
dc.contributor.authorSpokoiny, Vladimiren_US
dc.contributor.authorWang, Weiningen_US
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.publisherSFB 649, Economic Risk Berlinen_US
dc.relation.ispartofseriesSFB 649 discussion paper 2011-005en_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.stwNichtparametrisches Verfahrenen_US
dc.titleLocal quantile regressionen_US
dc.typeWorking Paperen_US
Appears in Collections:SFB 649 Discussion Papers, HU Berlin

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