Modeling of Functional Relationships of Regional Economic Systems Based on Small Samples Based on Bayesian Intelligent Measurements

Roman A. Zhukov 1, Svetlana V. Prokopchina 2, Maria A. Plinskaya 1, Maria A. Zhelunitsina 1

1 Tula Branch of The Financial University under the Government of the Russian Federation, Tula, Russia
2 The Financial University under the Government of the Russian Federation, Moscow, Russia

Abstract

Regional economies, which are economic subsystems of a socio-ecological-economic system, are characterized by rapidly changing conditions affecting their functioning. Therefore, traditional approaches based on long-term trends and the construction of appropriate models are not enough for modeling and need to be over updated, including through combination with other approaches that take into account the volatility of conditions. Open sources datasets are fraught with uncertainty (different sources may have different values for the same indicator). So, some indicators are being constantly adjusted: changes are introduced into datasets from the previous periods; evaluation periods are not sufficient for model construction that would be methodologically valid from the point of view of mathematical statistics, Additionally, the results of functioning depend on influencing factors as well as measurement results. These problems lead to the need to resort to probabilistic estimates and the incompleteness and fuzziness dataset, as well as to obtain samples of sufficient volume from low-power data. The purpose of the study is to model the relationship between the volume of gross domestic product and the factors characterizing the functioning of the economic subsystem of the regions, under the conditions of uncertainty and limited data in a given period of time. The case study is constructed on The Tula region dataset. The working hypothesis is that it is possible to construct a methodologically correct econometric models using the dataset of a specific region of the Russian Federation over a certain period of time. We applied the methods of econometric modeling and the methodology of Bayesian intelligent measurements, as well as the methodology for generating a sufficient amount of data on small samples. For the Tula region and dataset for 2022, nonlinear models for the GDP by region volumes (14 sections of the NACE) were built. This made it possible to test the presented methodology based on a combination of econometric and Bayesian approaches to modeling the complex systems functioning, with conclusions having both theoretical and practical value for the region's sustainable development. 

Keywords

gross domestic product by region; production function; socio-ecological-economic system; model; Bayesian intellectual measurements; small sample

JEL classification

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Acknowledgements

The study was carried out at the expense of a grant from the Russian Science Foundation № 24-28-20020, https://rscf.ru/project/24-28-20020/ and Tula region.

About Authors

Roman Aleksandrovich Zhukov

Doctor of Economics, Associate Professor, Researcher of The Financial University under the Government of the Russian Federation (Tula Branch), Tula, Russia (300012, Tula, Oruzheynaya street, 1a); ORCID https://orcid.org/0000-0002-2280-307X e-mail: pluszh@mail.ru

Svetlana Vasilievna Prokopchina

Doctor of Technical Sciences, Professor, Department of System Analysis in Economics, Financial University under the Government of the Russian Federation, Moscow, Russia (125167, Moscow, Leningradsky Prospekt, 49/2); ORCID https://orcid.org/0000-0001-5500-2781 e-mail: svprokopchina@mail.ru

Maria Aleksandrovna Plinskaya

Master Student, The Financial University under the Government of the Russian Federation (Tula Branch), Tula, Russia (300012, Tula, Oruzheynaya street, 1a); ORCID https://orcid.org/0000-0002-1307-0935 e-mail: plinskaya@gmail.com

Maria Anatolievna Zhelunitsina

Master Student, The Financial University under the Government of the Russian Federation (Tula Branch), Tula, Russia (300012, Tula, Oruzheynaya street, 1a); ORCID https://orcid.org/0009-0006-3129-2749 e-mail: maria202001@yandex.ru

For citation

Zhukov, R.A., Prokopchina, S.V., Plinskaya, M.A., Zhelunitsina, M.A. (2024). Modeling of Functional Relationships of Regional Economic Systems Based on Small Samples Based on Bayesian Intelligent Measurements. Journal of Applied Economic Research, Vol. 23, No. 3, 721-750. https://doi.org/10.15826/vestnik.2024.23.3.029

Article info

Received May 26, 2024; Revised June 18, 2024; Accepted June 22, 2024.

DOI: https://doi.org/10.15826/vestnik.2024.23.3.029

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