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Nonparametric multistep-ahead prediction in time series analysis

  • Rong Chen
  • Lijian Yang
  • Christian Hafner

We consider the problem of multistep-ahead prediction in time series analysis by using nonparametric smoothing techniques. Forecasting is always one of the main objectives in time series analysis. Research has shown that non-linear time series models have certain advantages in multistep-ahead forecasting. Traditionally, nonparametric "k"-step-ahead least squares prediction for non-linear autoregressive AR("d") models is done by estimating "E"("X" "t"+"k"  |"X" "t" , …,  "X" "t" - "d"+1 ) via nonparametric smoothing of "X" "t"+"k" on ("X" "t" , …, "X" "t" - "d"+1 ) directly. We propose a multistage nonparametric predictor. We show that the new predictor has smaller asymptotic mean-squared error than the direct smoother, though the convergence rate is the same. Hence, the predictor proposed is more efficient. Some simulation results, advice for practical bandwidth selection and a real data example are provided. Copyright 2004 Royal Statistical Society.

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Article provided by Royal Statistical Society in its journal Journal of the Royal Statistical Society Series B.

Volume (Year): 66 (2004)
Issue (Month): 3 ()
Pages: 669-686

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Handle: RePEc:bla:jorssb:v:66:y:2004:i:3:p:669-686
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