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Forecast horizon aggregation in integer autoregressive moving average (INARMA) models

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  • Mohammadipour, Maryam
  • Boylan, John E.
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    Abstract

    This paper addresses aggregation in integer autoregressive moving average (INARMA) models. Although aggregation in continuous-valued time series has been widely discussed, the same is not true for integer-valued time series. Forecast horizon aggregation is addressed in this paper. It is shown that the overlapping forecast horizon aggregation of an INARMA process results in an INARMA process. The conditional expected value of the aggregated process is also derived for use in forecasting. A simulation experiment is conducted to assess the accuracy of the forecasts produced by the aggregation method and to compare it to the accuracy of cumulative h-step ahead forecasts over the forecasting horizon. The results of an empirical analysis are also provided.

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    File URL: http://www.sciencedirect.com/science/article/pii/S0305048311001411
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    Bibliographic Info

    Article provided by Elsevier in its journal Omega.

    Volume (Year): 40 (2012)
    Issue (Month): 6 ()
    Pages: 703-712

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    Handle: RePEc:eee:jomega:v:40:y:2012:i:6:p:703-712

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    Related research

    Keywords: Discrete time series; INARMA model; Temporal aggregation; Cross-sectional aggregation; Forecast horizon aggregation; Yule−Walker estimation;

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    Cited by:
    1. Kourentzes, Nikolaos & Petropoulos, Fotios & Trapero, Juan R., 2014. "Improving forecasting by estimating time series structural components across multiple frequencies," International Journal of Forecasting, Elsevier, vol. 30(2), pages 291-302.

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