Research of the Dynamics of Russian GDP in the Conditions of Digitalization of the Economy

Lomakin, Nikolay; Radionova, Elena A.; Rybanov, Alexander Aleksandrovich; Mogharbel, Natalya; Vodopyanova, Natalia A.; Sycheva, Alexandra V.

doi:10.12731/2227-930x-2021-11-2-65-80

IJAS

2021

DOI: 10.12731/2227-930x-2021-11-2-65-80

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Research of the Dynamics of Russian GDP in the Conditions of Digitalization of the Economy

Nikolay Lomakin

Elena A. Radionova

Alexander Aleksandrovich Rybanov

et al.

Abstract: В ходе проведенного исследования выявлена нелинейная зависимость динамики ВВП от увеличения грузооборота. Разработана регрессионная модель, определяющая величину ВВП в зависимости от динамики грузооборота. Следует констатировать, что в России сложился довольно низкий уровень применения цифровых инноваций, по сравнению с зарубежными компаниями развитых стран и продолжает оставаться на низком уровне. Цель: Выдвинута и доказана гипотеза, что с помощью математической модели, можно получить прогноз ВВП России… Show more

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AI-system and multifactor correlation-regression model for forecasting RF GDP in the Colab and XL

Lomakin¹,

Peskova²,

Mershieva³

et al. 2022

The World Economics

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The article discusses the theoretical foundations for forecasting the gross domestic product (GDP) and the sustainability of the development of the Russian economy. The forecast values of GDP were calculated by two methods: based on the use of the AI-system and a multivariate correlationregression model for the purpose of subsequent comparison. The novelty of the study lies in the fact that a comparative assessment of the accuracy of the generated forecasted values of the RF GDP by different methods was carried out: using the AI system and a multivariate correlation-regression model in XLtables using the Microsoft Correlation and Regression packages. Some of the calculations were carried out in the Colab cloud environment using the Python programming language. The initial data for the AI-system and the correlation-regression model are statistical data — chain indices of the development of the main industries taken for the period 2000–2020. Studies have shown that the average forecast error calculated using a linear six-factor regression model between factorial features and the effective one was 0.153553 %, while the average forecast error calculated using the AI system was 0.010082 %. The forecasting accuracy using artificial intelligence algorithms is an order of magnitude higher than the traditional one — using a multivariate regression model. Based on the initial data, a dataset was collected, on which, using the Deductor analytical platform, an AI system was formed in order to obtain the forecast value of the RF GDP for the next calendar year. Using the capabilities of the Colab cloud service, in the Python language, the pairwise correlation coefficients between the factors were calculated, the results of which are presented in a heat matrix. Formed a correlation-regression model in XL-tables using the packages "Correlation" and "Regression" Microsoft. Also, the quality of the regression model, reflecting the dependence of the effective indicator "GDP" on the main factorial indicators, was assessed using the Student’s t-test, F-statistics and others.

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AI-system and multifactor correlation-regression model for forecasting RF GDP in the Colab and XL

Lomakin¹,

Peskova²,

Mershieva³

et al. 2022

The World Economics

View full text Add to dashboard Cite

show abstract

Forecasting the development of the domestic economy by the system of artificial intelligence, assessment of the risk and stability of the financial sector

Lomakin¹,

Meshcheryakova²,

Lukyanov³

et al. 2022

The World Economics

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The article considers theoretical approaches to forecasting the development of the domestic economy and the stability of the fi nancial sector. The work uses an artificial intelligence system developed by the authors. The paper puts forward and proves the hypothesis that the use of the risk assessment of the financial sector calculated using the VaR model in the neural network AI system can be used to obtain the forecast value of the country’s GDP in order to provide support for managerial decision-making under conditions of uncertainty. Of great importance is the study of the theoretical foundations for increasing the stability of the Russian economy in modern conditions. The use of neural network models in GDP forecasting is an important tool for assessing and ensuring the sustainability of the Russian economy. The novelty of the study lies in the fact that an approach is proposed to fill the gap that concerns the problem of the lack of a reliable approach in assessing the risk of financial sector stability in order to ensure the sustainable development of the domestic economy. Get a correct forecast of the gross domestic product (GDP) in the face of market uncertainty. According to the VaR model, with a 99 % probability, the absolute value of the fi nancial risk of a reduction in the profits of the banking system of the Russian Federation may amount to 108.5 billion rubles in 2022, or 4.5226 %, and the amount of profi t may reach 2.3 trillion rubles. rub. The resulting neuroforecast of the value of GDP for 2022 is 79 023.4 billion rubles. 33.3 % higher than the 59 262.95 billion rubles, declared by experts, the total forecast value of GDP for 1Q. (28410) and 2 sq. (30853). To compare the accuracy of the forecast, it remains to wait until the actual values of GDP become known. The practical significance of the study lies in the fact that in the course of the study the prerequisites for solving an important national economic problem were formed — forecasting the value of GDP and ensuring the sustainable development of the country’s economy.

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Research of the Dynamics of Russian GDP in the Conditions of Digitalization of the Economy

Cited by 2 publications

References 1 publication

AI-system and multifactor correlation-regression model for forecasting RF GDP in the Colab and XL

AI-system and multifactor correlation-regression model for forecasting RF GDP in the Colab and XL

Forecasting the development of the domestic economy by the system of artificial intelligence, assessment of the risk and stability of the financial sector

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