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Evaluating Approximate Point Forecasting of Count Processes

Author

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  • Annika Homburg

    (Department of Mathematics and Statistics, Helmut Schmidt University, 22043 Hamburg, Germany)

  • Christian H. Weiß

    (Department of Mathematics and Statistics, Helmut Schmidt University, 22043 Hamburg, Germany)

  • Layth C. Alwan

    (Sheldon B. Lubar School of Business, University of Wisconsin-Milwaukee, Milwaukee, WI 53211, USA)

  • Gabriel Frahm

    (Department of Mathematics and Statistics, Helmut Schmidt University, 22043 Hamburg, Germany)

  • Rainer Göb

    (Institute of Mathematics, Department of Statistics, University of Würzburg, 97070 Würzburg, Germany)

Abstract

In forecasting count processes, practitioners often ignore the discreteness of counts and compute forecasts based on Gaussian approximations instead. For both central and non-central point forecasts, and for various types of count processes, the performance of such approximate point forecasts is analyzed. The considered data-generating processes include different autoregressive schemes with varying model orders, count models with overdispersion or zero inflation, counts with a bounded range, and counts exhibiting trend or seasonality. We conclude that Gaussian forecast approximations should be avoided.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jecnmx:v:7:y:2019:i:3:p:30-:d:246272
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    References listed on IDEAS

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    Cited by:

    1. Annika Homburg & Christian H. Weiß & Gabriel Frahm & Layth C. Alwan & Rainer Göb, 2021. "Analysis and Forecasting of Risk in Count Processes," JRFM, MDPI, vol. 14(4), pages 1-25, April.
    2. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    3. Kachour Maher & Bakouch Hassan S. & Mohammadi Zohreh, 2023. "A New INAR(1) Model for ℤ-Valued Time Series Using the Relative Binomial Thinning Operator," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 243(2), pages 125-152, April.
    4. Annika Homburg & Christian H. Weiß & Layth C. Alwan & Gabriel Frahm & Rainer Göb, 2021. "A performance analysis of prediction intervals for count time series," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(4), pages 603-625, July.
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