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Forecasting integer autoregressive processes of order 1: are simple AR competitive?

Author

Listed:
  • Luisa Bisaglia

    (Department of Statistical Sciences - Padova University)

  • Margherita Gerolimetto

    (Department of Economics - Ca''''Foscari University Venice)

Abstract

In this work we want to clarify, via a Monte Carlo experiment, if (and when) for an integer-valued time series it is really recommended to adopt the coherent forecasting methods from INAR models or if equivalently good predictions can be obtained from the simpler AR models. Results show that INAR models should be preferred.

Suggested Citation

  • Luisa Bisaglia & Margherita Gerolimetto, 2015. "Forecasting integer autoregressive processes of order 1: are simple AR competitive?," Economics Bulletin, AccessEcon, vol. 35(3), pages 1652-1660.
    • Handle: RePEc:ebl:ecbull:eb-15-00073
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    References listed on IDEAS

    as
    1. Bu, Ruijun & McCabe, Brendan, 2008. "Model selection, estimation and forecasting in INAR(p) models: A likelihood-based Markov Chain approach," International Journal of Forecasting, Elsevier, vol. 24(1), pages 151-162.
    2. Jung, Robert C. & Tremayne, A.R., 2006. "Coherent forecasting in integer time series models," International Journal of Forecasting, Elsevier, vol. 22(2), pages 223-238.
    3. Robert Jung & Gerd Ronning & A. Tremayne, 2005. "Estimation in conditional first order autoregression with discrete support," Statistical Papers, Springer, vol. 46(2), pages 195-224, April.
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    Cited by:

    1. Annika Homburg & Christian H. Weiß & Layth C. Alwan & Gabriel Frahm & Rainer Göb, 2019. "Evaluating Approximate Point Forecasting of Count Processes," Econometrics, MDPI, vol. 7(3), pages 1-28, July.

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    More about this item

    Keywords

    Integer Autoregressive models; Forecasting;

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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