A Flexible Semiparametric Model for Time Series

Degui Li, Oliver Linton and Zudi Lu ()

No 17/12, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

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.

Keywords: Asymptotic normality; model averaging; Nadaraya-Watson kernel estimation; near epoch dependence; semiparametric method (search for similar items in EconPapers)
Date: 2012
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
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Related works:
Working Paper: A flexible semiparametric model for time series (2012)
Working Paper: A flexible semiparametric model for time series (2012)
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