Please use this identifier to cite or link to this item:
Li, Degui
Linton, Oliver
Lu, Zudi
Year of Publication: 
Series/Report no.: 
cemmap working paper CWP28/12
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.
asymptotic normality
model averaging
Nadaraya-Watson kernel estimation
near epoch dependence
semiparametric method
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

Files in This Item:
595.73 kB

Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.