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Моделювання Й Прогнозування Виробництва М’Яса Та Яєць В Україні За Допомогою Сезонної Arima-Моделі

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  • Zomchak, Larysa
  • Umrysh, Hryhorii

Abstract

The article investigates the problem of seasonality in the production of meat and eggs on the basis of the dynamics of time series of meat and egg production in Ukraine in 2009–2016. After the construction of the input time series in the stationary, parameters of the model were found and the production volumes of meat and eggs for the subsequent periods are predicted. The seasonal autoregressive economic and mathematical models such as SARIMA (seasonally ARIMA) were fitted on the basis of the time series describing the monthly dynamics of meat and egg production in Ukraine (based on statistics for the period 2009–2016). On the basis of these models, the forecasts of these indicators are received for the next two years. By comparing the obtained forecasts with the actual values, the conclusion is drawn about the adequacy of the results obtained.

Suggested Citation

  • Zomchak, Larysa & Umrysh, Hryhorii, 2017. "Моделювання Й Прогнозування Виробництва М’Яса Та Яєць В Україні За Допомогою Сезонної Arima-Моделі," Agricultural and Resource Economics: International Scientific E-Journal, Agricultural and Resource Economics: International Scientific E-Journal, vol. 3(3), September.
    • Handle: RePEc:ags:areint:263983
    DOI: 10.22004/ag.econ.263983
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    References listed on IDEAS

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    1. H. Theil & S. Wage, 1964. "Some Observations on Adaptive Forecasting," Management Science, INFORMS, vol. 10(2), pages 198-206, January.
    2. David F. Findley & Demetra P. Lytras & Agustin Maravall, 2016. "Illuminating ARIMA model-based seasonal adjustment with three fundamental seasonal models," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 7(1), pages 11-52, March.
    3. P. J. Harrison, 1967. "Exponential Smoothing and Short-Term Sales Forecasting," Management Science, INFORMS, vol. 13(11), pages 821-842, July.
    4. Hamid Moeeni & Hossein Bonakdari & Isa Ebtehaj, 2017. "Integrated SARIMA with Neuro-Fuzzy Systems and Neural Networks for Monthly Inflow Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(7), pages 2141-2156, May.
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