FORECASTING INDICATORS OF ECONOMIC SAFETY OF THE OMSK REGION IN THE MEDIUM-TERM PERSPECTIVE

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A. A. Korableva, Candidate of Economic Sciences, Head of the Sector for Regional Development Research Methods, Omsk Scientific Center of the Siberian Branch of the Russian Academy of Sciences,
15 K. Marx pros., Omsk, 644024, Russian Federation ORCID ID: https://orcid.org/0000-0003-4453-9748, Scopus Author ID: 57199327604,
Researcher ID: L-4304-2013, RSCI Author ID: 214697
e-mail: aakorableva@bk.ru
V. V. Karpov,
Doctor of Economics, Professor, Chairman of the Omsk Scientific Center of the Siberian Branch
of the Russian Academy of Sciences, 15 K. Marx pros., Omsk, 644024, Russian Federation
ORCID ID: https://orcid.org/0000-0002-1472-4873, Scopus Author ID: 57199325170,
Researcher ID: C-3801-2017, RSCI Author ID 338993,
e-mail: adm@oscsbras.ru

FORECASTING INDICATORS OF ECONOMIC SAFETY  OF  THE  OMSK  REGION IN THE MEDIUM-TERM PERSPECTIVE

Acknowledgments: The work was carried out according to the state task of the OSC SB RAS

(project registration number АААА-А17-117041210229-2)

Introduction. The economic safety of the region is one of the factors of its sustainable socio- economic development. Forecasting indicators of the economic safety provides information about the pro- spects and threats to regional development. Based on these forecasts, regional authorities promote in the organization of business projects, make decisions about the necessary actions and amounts of budget funds. In this regard, the task of improving the accuracy of forecasts of socio-economic indicators is very actual. The purpose of the article is to estimate the correctness of the use of autoregression and moving average models for forecast indicators of economic safety on example of the Omsk Region.

Materials and methods. Mathematical methods of forecasting using autoregressive and integrated moving average models are considered. The procedure of identification of models and research of their va- lidity is regarded. The forecast and actual values of economic safety indicators are compared. Using the induction method, is approved the possibility of using autoregressive and integrated moving average mod- els for forecast the economic safety of the region in the economic and social scopes.

Results. The forecast models for indicators of economic safety are constructed. According to the re- sults there will be growth gross regional product per capita, volume of industrial and agricultural production and paid services in the Omsk Region. The degree of depreciation of fixed assets will increase minor. For retail trade per capita the forecast confidence interval is too wide, which shows a high level of uncertainty of the presented forecast. The results of forecasting indicators in the social scope remain controversial, the ARIMA models is most adequate only for the indicators «average per capita cash of the population» and

«total area of residential premises per person on average». In all other cases, there is too much variation in the boundaries of the confidence intervals.

Conclusions. The research of the adequacy of the models showed that the autoregressive and inte- grated moving average models describe economic indicators quite well, which can be used in drawing up plans for the economic development of the region in the medium term. On the contrary, the use of these models for forecasting social indicators showed a high level of uncertainty in the predicted values, which requires the development of a methodology for improves the accuracy of forecasting indicators of regional economic safety. This technique can be based on ARIMA models with intervention.

Keywords: economic safety of the region, regional development forecasting, autoregression model, moving average model, SPSS Statistics.

References

  1. On the Strategy of Economic Security of the Russian Federation for the period up to 2030. SPS Konsul’tant plyus» (accessed: 05.09.2018). (In Russian).
  2. Theory and practice of economic security assessment (on the example of the regions of the Siberian Federal District). Ed. by V. V. Karpov, A. A. Korableva. Novosibirsk, IEOPP SO RAN Publ., 2017. 146 p. (In Russian).
  3. Vanchikova E. N., Lygdenova T. B., Makarov A. N. Forecasting in the system of measures for to ensure eco- nomic security of the region. Bulletin of the Buryat State University. Ulan-Ude, 2015. Vol. 2S. pp. 275–280. (In Russian).
  4. Dudin M. N., Lyasnikov N. V. Food security of regions in the national and economic security of the State.
  5. Regional economy: theory and practice. Moscow, 2015. Vol. 6 (381). pp. 2–11. (In Russian).
  6. Yandybaeva N. V., Kushnikov V. A. Mathematical model for forecasting of indicators of economic safety of the Russian Federation. Bulletin of Astrakhan State Technical University. Series: Management, Computer Engi- neering, and Computer Science. Astrakhan, 2014. Vol. 3. pp. 93–101. (In Russian).
  7. Usmanova T. H. Planning and forecasting of innovative projects to ensure economic security in terms of in- tegration of economies into the world economy. Economy: yesterday, today, tomorrow. 2017. T. 7. Vol. 2A. pp. 186– 199. (In Russian).
  8. Stovba E. V. Scenario forecasting as a design instrument of the development strategy for the agrarian food complex of the region. Russian electronic scientific journal. 2017. Vol. 3 (25). pp. 71–84. (In Russian).
  9. Narayan S. Predictability within the energy consumption-economic growth nexus: Some evidence from in- come and regional groups. Economic Modelling. 2016. Vol. 54. pp. 515–521. DOI: 10.1016/j.econmod.2015.12.037
  10. Panahi R., Ghasemi Koohi Kheili A., Golpira A. Future of Container Shipping in Iranian Ports: Traffic and Connectivity Index Forecast. Journal of Advanced Transportation. 2017. Article ID 5847372, 13 p. DOI: 10.1155/2017/5847372
  11. Wang Z., Yang L., Yin J., Zhang B. Assessment and prediction of environmental sustainability in China based on a modified ecological footprint model. Resources, Conservation and Recycling. 2018. Vol. 132. pp. 301– 313. DOI: 10.1016/j.resconrec.2017.05.003
  12. Dritsaki C. The performance of hybrid ARIMA-GARCH modeling and forecasting oil price. International Journal of Energy Economics and Policy. 2018. Vol. 8(3). pp. 14–21. URL: https://www.econjournals. com/index.php/ijeep/article/view/6437/3672
  13. Boks D., Dzhenkins G. Time series analysis. Forecast and control. Moscow, Mir Publ., 1974. 406 p. (In Rus- sian).
  14. Bendat D., Pirsol A. Applied analysis of random data. Moscow, Mir Publ., 1989. 540 p. (In Russian).
  15. Mityakov E. S., Sazontov V. A. Application of adaptive filtration algorithms to forecast economic dynam- ics. Trudy NGTU im. R. E. Alekseeva. Nizhny Novgorod, 2012. Vol. 2 (95). pp. 339–344. (In Russian).
  16. Loginov K. K., Korableva A. A., Karpov V. V. Economic security of the Siberian federal district regions. The Science of Person: Humanitarian Researches. Omsk, 2018. Vol. 1 (31). pp. 141–150. (In Russian).
  17. Regions of Russia: socio-economic indicators. Federal’naya sluzhba gosudarstvennoi statistiki. Moscow. URL: http://www.gks.ru/wps/wcm/connect/rosstat_main/rosstat/ru/statistics/publications/catalog/doc_1138623506156 (In Russian).
  18. Omsk Region in Figures: A Brief Statistical Digest. Omskstat. Omsk, 2018. 40 p. (In Russian).
  19. Patlasov O. Yu., Ivlev K. I. Methods of assessment of national and regional food security. Siberian village: history, current state, development prospects. Materials V1 International. scientific and practical. Conf. 30- 31.03.2006. Vol. 3. Omsk. 2006. pp. 99–104.