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• W2100024019 abstract "We introduce a two-step procedure for more efficient nonparametricprediction of a strictly stationary process admitting an ARMArepresentation. The procedure is based on the estimation of the ARMArepresentation, followed by a nonparametric regression where theARMA residuals are used as explanatory variables. Compared tostandard nonparametric regression methods, the number of explanatoryvariables can be reduced because our approach exploits the lineardependence of the process. We establish consistency and asymptoticnormality results for our estimator. A Monte Carlo study and anempirical application on stock market indices suggest thatsignificant gains can be achieved with our approach." @default.
• W2100024019 created "2016-06-24" @default.
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• W2100024019 date "2009-01-01" @default.
• W2100024019 modified "2023-10-16" @default.
• W2100024019 title "Combining parametric and nonparametric approaches for more efficient time series prediction" @default.
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