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dc.contributor.authorLi, Deguien_US
dc.contributor.authorLinton, Oliveren_US
dc.contributor.authorLu, Zudien_US
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_US
dc.publisher|aCentre for Microdata Methods and Practice (cemmap) |cLondonen_US
dc.relation.ispartofseries|acemmap working paper |xCWP28/12en_US
dc.subject.keywordasymptotic normalityen_US
dc.subject.keywordmodel averagingen_US
dc.subject.keywordNadaraya-Watson kernel estimationen_US
dc.subject.keywordnear epoch dependenceen_US
dc.subject.keywordsemiparametric methoden_US
dc.titleA flexible semiparametric model for time seriesen_US
dc.typeWorking Paperen_US

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