A Flexible Semiparametric Model for Time Series

45 Pages Posted: 6 Aug 2012

See all articles by Oliver B. Linton

Oliver B. Linton

University of Cambridge

Degui Li

University of York

Zudi Lu

University of Southampton

Date Written: August 6, 2012

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

JEL Classification: C14, C22

Suggested Citation

Linton, Oliver B. and Li, Degui and Lu, Zudi, A Flexible Semiparametric Model for Time Series (August 6, 2012). Available at SSRN: https://ssrn.com/abstract=2125133 or http://dx.doi.org/10.2139/ssrn.2125133

Oliver B. Linton

University of Cambridge ( email )

Faculty of Economics
Cambridge, CB3 9DD
United Kingdom

Degui Li (Contact Author)

University of York ( email )

Deparment of Mathematics
University of York
Heslington, York YO10 5DD
United Kingdom

Zudi Lu

University of Southampton ( email )

University Rd.
Southampton SO17 1BJ, Hampshire SO17 1LP
United Kingdom

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