Control Systems and Computers

https://doi.org/10.15407/csc.2021.04.035

Control Systems and Computers, 2021, Issue 4 (294), pp. 35-50.

UDK 681.513; 364.2:331

Yevgeniya Savchenko-Synyakova
Department for Information Technologies of Inductive Modelling International Research and Training Centre for Information Technologies and Systems of the NAS and MES, Kyiv, Ukraine, savchenko_e@meta.ua,

Olena Tutova
Department of Economics and Social Systems and Information Technology International Research and Training Centre for Information Technologies and Systems of the NAS and MES, Kyiv, Ukraine, sir_ludovick@yahoo.com,

Halyna Pidnebesna
Department for Information Technologies of Inductive Modelling International Research and Training Centre for Information Technologies and Systems of the NAS and MES, Kyiv, Ukraine, pidnebesna@ ukr.net

Application of the CORAL GMDH Algorithm for Modeling and Forecasting the National Income of Ukraine

CORAL GMDH is a method of the inductive approach. In this article, it is used for modeling and forecasting socio-economic processes. Here the CORAL GMDH algorithm is applied to solve three problems: recovery of missing data, modeling of macroeconomic indicators, and forecasting the gross national income (GNI). Also, the CORAL GMDH algorithm is used to build models in the problem of modeling the dependence of GNI on socio-demographic indicators and develop recommendations on how the state can influence the level of human capital development in the country by influencing certain socio-demographic indices. All three tasks have been solved for Ukraine, Belarus, and Poland.

 Download full text! (On English)

Keywords: recovery of missing data, modeling, forecasting, gross national income per capita, CORAL GMDH, domestic health spending,  socio-demographic indicators.

1. Resolution of the Verkhovna Rada of Ukraine. On the Recommendations of the Parliamentary Hearings on the Subject: “Balanced Development of Human Capital in Ukraine: Tasks of Education and Science”. [online] Available at: <http://search.ligazakon.ua/l_doc2.nsf/link1/DH7YU00A.html> [Accessed 29 August 2019]. (In Ukrainian).
2. Human capital development. The Cabinet of Ministers of Ukraine. [online] Available at: <https://www.kmu.gov.ua/diyalnist/reformi/rozvitok-lyudskogo-kapi-talu> [Accessed 2 2019]. (In Ukrainian).
3. Human Development Report 2015. Working for Human. [online] Available at: <http://www.un-ilibrary.org/economic-and-social-development/2015_a38c8a2e-ru> [Accessed 6 Feb. 2019] (In Russian).
4. Development of human capital: on the way to quality reforms, 2018. K .: Razumkov Center. Zapovit Publishing House, 367 p. (In Ukrainian).
5. Shakhno, A.Y., 2018. “Laws of human capital development in the context of globalization”. International economic relations and the world economy. Issue 20, part 3, pp. 139-143. (In Ukrainian).
6. Nikiforenko, V., 2012. “Forecasting the development of human potential in the context of global trends”. Ukraine: aspects of labor, n 1, pp. 14-19. (In Ukrainian).
7. Sakharovsky, C.N., 2012. “Institutional factors in the formation of human potential. Journal of Institutional Studies”, Vol. 4, No. 2, pp. 83-92 (In Russian).
8. Human capital: how to measure and increase its value. [online] Available at: <https://blog.iteam.ru/chelovecheskij-kapital-kak-izmerit-i-uvelichit-ego-stoimost/> [Accessed 16 Apr. 2020] (In Russian).
9. Andreev, Yu.N. Human capital in an innovative economy. [online] Available at: <http://regions.extech.ru ›publication› andreev> [Accessed 16 Apr. 2020] (In Russian).
10. Aslanov D.I. Human capital – the most important factor of economic growth. [online] Available at: < https://cyberleninka.ru/article/n/chelovecheskiy-kapital-vazhneyshiy-faktor-ekonomicheskogo-rosta> [Accessed 6 Apr. 2020] (In Russian).
11. Human resource or human capital? / Elena Shulgina, Elena Sosnovtseva. Community of HR and HR specialists, 2014. [online] Available at: <https://hrliga.com/index.php?module=news&op=view&id=11832> [Accessed 16 Apr. 2020] (In Russian).
12. Shinkaryuk, O.V., Stolyarov, V.F., Ostrovetsky, V.I., 2016. “Global and National no1, pp. 171-182 (In Russian).
13. Tutova, O.V., 2012. “Conceptual bases of human capital formation”. Economic and mathematical modeling of socio-economic systems. Collection of scientific works. Vyp. Answer. ed. Dr. those. Sciences, Professor L.A. Ponomarenko. Kyiv: International Research and Training Center for Information Technologies and Systems of the National Academy of Sciences of Ukraine and the Ministry of Education and Science of Ukraine, 379 p, pp. 119-129 (In Ukrainian).
14. Kean Birch 1, Les Levidow and Theo Papaioannou. Sustainable Capital? “The Neoliberalization of Nature and Knowledge in the European “Knowledge-based Bio-economy” – Sustainability 2010″, 2, 13 September 2010, pp. 2899-2918.
    https://doi.org/10.3390/su2092898
15. Tutova, O.V., 2017. “Socially Responsible Investment in Human Capital”. Economic and mathematical modeling of socio-economic systems: nauk. prats, Vyp. 22. K.: IRTC ITS, pp. 149-159.
16. The Sustainable Development Goals Report 2017. United Nations publication issued by the Department of economic and Social Affairs (DeSA. [online] Available at: <https://unstats.un.org/sdgs> [Accessed 16 Dec. 2020].
17. Savchenko-Synyakova, Ye.A., Tutova, O.V., 2021. “The Tools for Intelligent Data Analysis, Modeling and Forecasting of Social and Economic Processes”. Control Systems and Computers, 2021, 1 (291), pp. 35-45.
    https://doi.org/10.15407/csc.2021.01.035
18. Intelligent Modelling and Analysis. [online] Available at: <https://www.nottingham.ac.uk/computerscience/research/ima.aspx> [Accessed 16 Dec. 2020].
19. Advances in Intelligent Modelling and Simulation: Artificial Intelligence-Based Models and Techniques in Scalable Computing / Eds J. Kołodziej, S.U. Khan, T. Burczyński. Berlin–Heidelberg: Springer-Verlag, 2012. 381 p.
20. Opportunities in Intelligent Modeling Assistance. [online] Available at: <https://modeling-languages.com/opportunities-intelligent-modeling-assistance/> [Accessed 11 Dec. 2020].
21. Intelligent modeling of historical and as-built plant data for an efficient decision making. [online] Available at: <https://www.avineon.com/en/intelligent-data-modeling> [Accessed 16 Dec. 2020].
22. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Global Health Metrics. Lancet 2020; 396: 1204–22.
23. Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet 2020; 396: 1135–59.
24. Global age-sex-specific fertility, mortality, healthy life expectancy (HALE), and population estimates in 204 countries and territories, 1950–2019: a comprehensive demographic analysis for the Global Burden of Disease Study 2019. Lancet 2020; 396: 1135–59.
25. Fertility, mortality, migration, and population scenarios for 195 countries and territories from 2017 to 2100: a forecasting analysis for the Global Burden of Disease Study. Lancet 2020; 396: 1285-306. Published Online July 14, 2020 https://doi.org/10.1016/ S0140-6736(20)30677-2.
    https://doi.org/10.1016/S0140-6736(20)30677-2
26. Health sector spending and spending on HIV/AIDS, tuberculosis, and malaria, and development assistance for health: progress towards Sustainable Development Goal 3. Lancet 2020; 396: 693–724. Published Online April 23, 2020 https://doi.org/10.1016/ S0140-6736(20)30608-5.
27. Measuring universal health coverage based on an index of effective coverage of health services in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet 2020; 396: 1250–84. Published Online August 27, 2020, https://doi.org/10.1016/ S0140-6736(20)30750-9.
28. World Health Organization. (‎2020)‎. Global spending on health 2020: weathering the storm. World Health Organization. [online] Available at: https://apps.who.int/iris/handle/10665/337859. License: CC BY-NC-SA 3.0 IGO> [Accessed 11 Dec. 2020].
29. Violence and injuries in Europe: burden, prevention and priorities for action (2020). [online] Available at: <https://www.euro.who.int/en/health-topics/disease-prevention/violence-and-injuries/publications/2020/violence-and-injuries-in-europe-burden,-prevention-and-priorities-for-action-2020
30. Global status report on alcohol and health. [online] Available at: <https://www.who.int/news-room/fact-sheets/detail/alcohol> [Accessed 11 Dec. 2020].
31. Newborn death and illness. [online] Available at: <https://www.who.int/pmnch/media/press_materials/fs/fs_newborndealth_illness/en/> [Accessed 11 Dec. 2020].
32. Tutova O., Savchenko Ie., 2013. “Modeling of Impact of Macroeconomic Indicators on the Growth of National Income”. Proceedings of 4th International Conference in Inductive Modelling ICIM 2013, Kyiv: IRTC, pp. 315-319.
33. Tutova O., Savchenko Ye., 2015. The impact of demographic and social indicators on gross regional product in the regions of Ukraine. Inductive modeling of complex systems. Zb. prats. K.: MNNTS IT ta S, 7, pp. 271-281. (In Ukrainian).
34. Tutova O., Savchenko E., 2013. “Research of Reasons for Human Development Index Growth by GMDH”. Inductive modeling of complex systems. prats. K.: MNNTS IT ta S, 5, pp. 120-129. (In Ukrainian).
35. Pidnebesna, H.A, “Bioproductivity of Dnieper Reservoirs Analysis by Inductive Methods”. Control Systems and Computers, 2, pp. 66-76.
    https://doi.org/10.15407/csc.2020.02.066
36. Pidnebesna, H., Stepashko, V., 2020. “Comparative Effectiveness of Some Approaches to Extracting Most Informative Factors Influencing Algae Bioproductivity”. Proceedings of the Int. Conf. “Advanced Computer Information Technologies” ACIT-2020, Deggendorf, Germany, September 15-18, 2020, pp. 257-260.
    https://doi.org/10.1109/ACIT49673.2020.9208957
37. Ivakhnenko, A.G., Savchen”ko, E.A., Ivakhnenko, G.A., Gergely, T., 2002. “Application of MGUA algorithms to recover missing data and predict blood glucose levels in home-based diabetes monitoring”. Control and Informatics Problems, no 3, pp. 123–133 (In Russian).

Received 28.09.2021