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Forecasting Randomly Distributed Zero-Inflated Time Series

Author

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  • Doszyń Mariusz

    (University of Szczecin Faculty of Economics and Management Institute of Econometrics and Statistics Mickiewicza 64, 71-101 Szczecin, Poland)

Abstract

The main aim of the article is to propose a forecasting procedure that could be useful in the case of randomly distributed zero-inflated time series. Many economic time series are randomly distributed, so it is not possible to estimate any kind of statistical or econometric models such as, for example, count data regression models. This is why in the article a new forecasting procedure based on the stochastic simulation is proposed. Before it is used, the randomness of the times series should be considered. The hypothesis stating the randomness of the times series with regard to both sales sequences or sales levels is verified. Moreover, in the article the ex post forecast error that could be computed also for a zero-inflated time series is proposed. All of the above mentioned parts were invented by the author. In the empirical example, the described procedure was applied to forecast the sales of products in a company located in the vicinity of Szczecin (Poland), so real data were analysed. The accuracy of the forecast was verified as well.

Suggested Citation

  • Doszyń Mariusz, 2017. "Forecasting Randomly Distributed Zero-Inflated Time Series," Folia Oeconomica Stetinensia, Sciendo, vol. 17(1), pages 7-19, June.
    • Handle: RePEc:vrs:foeste:v:17:y:2017:i:1:p:7-19:n:1
    DOI: 10.1515/foli-2017-0001
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    References listed on IDEAS

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    1. Hilbe,Joseph M., 2014. "Modeling Count Data," Cambridge Books, Cambridge University Press, number 9781107611252, October.
    2. Rainer Winkelmann, 2008. "Econometric Analysis of Count Data," Springer Books, Springer, edition 0, number 978-3-540-78389-3, June.
    3. Biswas, Atanu & Song, Peter X.-K., 2009. "Discrete-valued ARMA processes," Statistics & Probability Letters, Elsevier, vol. 79(17), pages 1884-1889, September.
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