Methodological Approach to Forecasting Economic Development of Agrarian Sector in Cherkasy Region

Authors

  • Nadiia P. Reznik
  • Nataliia O. Petrenko
  • Anastasiia V. Movchaniuk
  • Mariya M. Pokolodna
  • Vira F. Nevlad V

Abstract

In the process of scheming scenarios for the development of agro-industrial production of the region, the method of scenario analysis was used for certain factors, outlined the possible states in which the main factors may be selected, set many of all possible configurations of states. Since the two most significant factors are identified, according to the formula for calculating the power of the morphological space, it is possible to generate four different configurations of states that are irreconcilable and can be realized in the long run.
Based on the constructed topology, it is revealed that with the implementation of scenario K1, the largest growth of gross regional product (5%) can be achieved provided that such factors as growth of capital investments in agriculture, growth of gross agricultural output, growth of capital investments in food industry are constantly increasing. If capital investment is sufficient to ensure simple reproduction (increase by 15-20%), agricultural output (more than 15%), capital investment in the food industry (more than 10%), then the gross regional product will grow by 2%. The probability of such development is 60%.
The results of the study showed that investments in the volume of not less than 40 percent of the production volume should be attracted to the expanded reproduction in the economy. For the simple one – at the level of 30 percent. This assumption was the basis of the forecast to determine the economic development prospects of the agricultural sector in the region. The information base of the study is the most complete time series of significant indicators of socio-economic development of the Cherkasy region for 2007–2016. It should be noted that the most significant are the free member of the model and the first principal component, with a coefficient of determination equal to 0.85, i.e. the model takes into account 85% of the change in the data. The model has acceptable characteristics: SKP = 0.02, SKV = 0.04, and provides the proper quality of the forecast.

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Published

2020-04-09

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Articles