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Forecasting highly persistent time series with bounded spectrum processes

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  • Federico Maddanu

    (CY University
    University of Rome Tor Vergata)

Abstract

Long memory models can be generalised by the Fractional equal-root Autoregressive Moving Average (FerARMA) process, which displays short memory for a suitable parameter’s set. Consequently, the spectrum is bounded, ensuring stationarity also for values of the memory parameter d larger than 0.5. The FerARMA generalization is proposed here to forecast highly persistent time series, as climate records of tree rings and paleo-temperature reconstructions. The main advantage of a bounded spectrum allows for more accurate predictions with respect to standard long memory models, especially if a long prediction horizon is considered.

Suggested Citation

  • Federico Maddanu, 2023. "Forecasting highly persistent time series with bounded spectrum processes," Statistical Papers, Springer, vol. 64(1), pages 285-319, February.
    • Handle: RePEc:spr:stpapr:v:64:y:2023:i:1:d:10.1007_s00362-022-01321-z
    DOI: 10.1007/s00362-022-01321-z
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    References listed on IDEAS

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