Nonparametric multistep‐ahead prediction in time series analysis

Rong Chen, Lijian Yang and Christian Hafner

Journal of the Royal Statistical Society Series B, 2004, vol. 66, issue 3, 669-686

Abstract: Summary. 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(Xt+k |Xt, …, Xt−d+1) via nonparametric smoothing of Xt+k on (Xt, …, Xt−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.

Date: 2004
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Citations: View citations in EconPapers (14)

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https://doi.org/10.1111/j.1467-9868.2004.04664.x

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Working Paper: Nonparametric multistep-ahead prediction in time series analysis (2004)
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