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Title:A flexible semiparametric model for time series PDF Logo
Authors:Li, Degui
Linton, Oliver
Lu, Zudi
Issue Date:2012
Series/Report no.:cemmap working paper CWP28/12
Abstract:We 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.
Subjects:asymptotic normality
model averaging
Nadaraya-Watson kernel estimation
near epoch dependence
semiparametric method
JEL:C14
C22
Persistent Identifier of the first edition:doi:10.1920/wp.cem.2012.2812
Document Type:Working Paper
Appears in Collections:cemmap working papers, Centre for Microdata Methods and Practice, Institute for Fiscal Studies (IFS)

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