A flexible semiparametric forecasting model for time series
A flexible semiparametric forecasting model for time series
In this paper, we propose a semiparametric procedure called the “Model Averaging MArginal Regression” (MAMAR) that is flexible for forecasting of time series. This procedure considers approximating a multivariate regression function by an affine combination of one-dimensional marginal regression functions. The weight parameters involved in the approximation are estimated by least squares on the basis of the first-stage nonparametric kernel estimates of the marginal regressions. Under some mild conditions, we have established asymptotic normality for the estimated weights and the regression function in two cases: Case I considers that the number of the covariates is fixed while Case II allows the number of the covariates depending on the sample size and diverging. As the observations are assumed to be stationary and near epoch dependent, the approach developed is applicable to both the estimation and forecasting issues in time series analysis. Furthermore, the method and result are augmented by a simulation study and illustrated by an application in forecasting the high frequency volatility of the FTSE100 index.
forecasting, marginal regression, model averaging, kernel estimation, near epoch dependence, semiparametric estimation
345-357
Li, Degui
e341f702-23cd-4c1a-91a8-3b7aa3dfda15
Linton, Oliver
36fa04bf-d781-45bd-bd7f-c28f68e5df6e
Lu, Zudi
4aa7d988-ac2b-4150-a586-ca92b8adda95
July 2015
Li, Degui
e341f702-23cd-4c1a-91a8-3b7aa3dfda15
Linton, Oliver
36fa04bf-d781-45bd-bd7f-c28f68e5df6e
Lu, Zudi
4aa7d988-ac2b-4150-a586-ca92b8adda95
Li, Degui, Linton, Oliver and Lu, Zudi
(2015)
A flexible semiparametric forecasting model for time series.
Journal of Econometrics, 187 (1), .
(doi:10.1016/j.jeconom.2015.02.025).
Abstract
In this paper, we propose a semiparametric procedure called the “Model Averaging MArginal Regression” (MAMAR) that is flexible for forecasting of time series. This procedure considers approximating a multivariate regression function by an affine combination of one-dimensional marginal regression functions. The weight parameters involved in the approximation are estimated by least squares on the basis of the first-stage nonparametric kernel estimates of the marginal regressions. Under some mild conditions, we have established asymptotic normality for the estimated weights and the regression function in two cases: Case I considers that the number of the covariates is fixed while Case II allows the number of the covariates depending on the sample size and diverging. As the observations are assumed to be stationary and near epoch dependent, the approach developed is applicable to both the estimation and forecasting issues in time series analysis. Furthermore, the method and result are augmented by a simulation study and illustrated by an application in forecasting the high frequency volatility of the FTSE100 index.
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Accepted/In Press date: 2 February 2015
e-pub ahead of print date: 25 March 2015
Published date: July 2015
Keywords:
forecasting, marginal regression, model averaging, kernel estimation, near epoch dependence, semiparametric estimation
Organisations:
Statistical Sciences Research Institute
Identifiers
Local EPrints ID: 381486
URI: http://eprints.soton.ac.uk/id/eprint/381486
ISSN: 0304-4076
PURE UUID: 78b2fc92-6dfc-4d95-a1f7-43826e8da983
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Date deposited: 09 Oct 2015 16:01
Last modified: 15 Mar 2024 03:49
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Author:
Degui Li
Author:
Oliver Linton
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