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dc.contributor.authorLi, Deguien
dc.contributor.authorLinton, Oliveren
dc.contributor.authorLu, Zudien
dc.date.accessioned2012-09-24-
dc.date.accessioned2012-10-16T13:09:21Z-
dc.date.available2012-10-16T13:09:21Z-
dc.date.issued2012-
dc.identifier.pidoi:10.1920/wp.cem.2012.2812en
dc.identifier.urihttp://hdl.handle.net/10419/64711-
dc.description.abstractWe consider approximating a multivariate regression function by an affine combination of one-dimensional conditional component regression functions. The weight parameters involved in the approximation are estimated by least squares on the first-stage nonparametric kernel estimates. We establish asymptotic normality for the estimated weights and the regression function in two cases: the number of the covariates is finite, and the number of the covariates is diverging. As the observations are assumed to be stationary and near epoch dependent, the approach in this paper is applicable to estimation and forecasting issues in time series analysis. Furthermore, the methods and results are augmented by a simulation study and illustrated by application in the analysis of the Australian annual mean temperature anomaly series. We also apply our methods to high frequency volatility forecasting, where we obtain superior results to parametric methods.en
dc.language.isoengen
dc.publisher|aCentre for Microdata Methods and Practice (cemmap) |cLondonen
dc.relation.ispartofseries|acemmap working paper |xCWP28/12en
dc.subject.jelC14en
dc.subject.jelC22en
dc.subject.ddc330en
dc.subject.keywordasymptotic normalityen
dc.subject.keywordmodel averagingen
dc.subject.keywordNadaraya-Watson kernel estimationen
dc.subject.keywordnear epoch dependenceen
dc.subject.keywordsemiparametric methoden
dc.titleA flexible semiparametric model for time series-
dc.typeWorking Paperen
dc.identifier.ppn726303968en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen
dc.identifier.repecRePEc:ifs:cemmap:28/12en

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