A flexible semiparametric model for time series

Degui Li, Oliver Linton and Zudi Lu
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Zudi Lu: Institute for Fiscal Studies

No CWP28/12, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies

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)
JEL-codes: C14 C22 (search for similar items in EconPapers)
Date: 2012-09-21
New Economics Papers: this item is included in nep-ets, nep-for and nep-ore
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Citations: View citations in EconPapers (1)

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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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