Scenario forecasting of the dynamics of Russian production technologies using spatial SAR models

Ilya V. Naumov, Sergey S. Krasnykh

Abstract


Relevance. The development and implementation of advanced production technologies are the most important factors of economic growth and competitiveness in the modern economy. Predicting their dynamics, taking into account the spatial features of localization, is a difficult and time-consuming task. The spatial effects resulting from the impact of the surrounding territories play a significant role in the dynamics of advanced production technologies in the regions of Russia. Accounting for these effects is necessary when constructing scenario models in conditions of strong spatial heterogeneity of the studied processes. Traditional forecasting methods do not take into account spatial interdependencies and are not able to reflect the influence of surrounding regions on the development of technologies.

Research objective. Assessment and scenario forecasting of the dynamics of advanced production technologies being developed in the regions of Russia using SAR models that allow taking into account spatial effects between regions.

Data and methods. For scenario forecasting of the dynamics of advanced production technologies being developed in the Russian regions, taking into account spatial effects, a methodological approach was developed based on the modeling of the spatial log (SAR) of the processes of their development, autoregressive (ARMA) modeling and forecasting of the key factors of their dynamics. Taking into account spatial effects and heterogeneity, the proposed approach to modeling makes it possible to more accurately predict the dynamics of advanced production technologies in the Russian regions.

Results. The developed methodological approach was tested to form predictive scenarios for the dynamics of advanced production technologies being developed in the regions of Russia. In particular, an inertial forecast scenario was developed, assuming the preservation of current trends in the dynamics of the technologies being developed, as well as two extreme possible scenarios – optimistic and pessimistic. With the help of the spatial SAR model, a significant influence of the number of research organizations on the volume of advanced production technologies generated was confirmed, and in the second group of regions, the influence of the number of technicians who conduct research and development was confirmed.

The novelty of the study is to take into account the spatial features of the localization of the advanced production technologies being developed, as well as the spatial effects resulting from the impact of the surrounding regions on the creation of new technologies. This approach makes it possible to significantly reduce errors in the formation of forecast scenarios in conditions of significant spatial heterogeneity of the initial data.

Conclusions. To intensify the generation of new technologies in the regions of the second group, it is necessary to attract personnel with technical specialties. The dynamics of the technologies being developed in the first group of regions with a powerful research potential are also influenced by the number of research personnel and the amount of attracted financial resources for fundamental and applied research. To increase the activity of these regions in the development of advanced technologies, it is necessary to form and develop relationships with the surrounding regions.


Keywords


advanced production technologies, science, technologies, modeling, spatial SAR model

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References


Ahmad, N., Ghadi Y., Adnan M., Ali M. (2022) Load Forecasting Techniques for Power System: Research Challenges and Survey. IEEE Access, 10, 71054–71090. DOI: 10.1109/ACCESS.2022.3187839

Aulia, S., Sirait, H. (2023). Modeling Life Expectation of Population in Sumatra Island Using Durbin Spatial Model Analysis. International Journal of Mathematics, Statistics, and Computing, 1(3), 35–43. DOI: 10.46336/ijmsc.v1i3.7

Brady, M., Irwin, E. (2011). Accounting for Spatial Effects in Economic Models of Land Use: Recent Developments and Challenges Ahead. Environ Resource Econ. 48, 487–509. DOI: 10.1007/s10640-010-9446-6

Brilliantova, V.V., Vlasova, V.V., Fursov, K.S. (2020) Technological Diversity and Self-Sufficiency in Advanced Production Technologies in Russian Regions. Jekonomika regiona = Economy of the region, 4, 1224–1238. (In Rus). DOI: 10.17059/ekon.reg.2020-4-15

Bokun, K.O., Jackson, L.E., Kliesen, K.L., Owyang, M.T. (2021) FRED-SD: A RealTime Database for State-Level Data with Forecasting Applications. Federal Reserve Bank of St. Louis Working Paper, 2020–031. DOI: 10.20955/wp.2020.031

Chudik, A., Pesaran, M. (2013). Large Panel Data Models with Cross-Sectional Dependence: A Survey. CAFE Research Paper, 13(15), 2316333 DOI: 10.2139/ssrn.2316333

Dang, W., Zhu, M. (2022) Regional Heterogeneity of National-Level New Areas on Economic Development – An Empirical Study Based on DID Model. Proceedings of the International Conference on Information Economy. Data Modeling and Cloud Computing, ICIDC 2022: 17-19 June 2022, Qingdao, China. DOI: 10.4108/eai.17-6-2022.2322888

Deng, Y, Li, X, Zhu, J. (2024). Effect of Planning and Construction of Intercity Railways on the Economic Development of the Pearl River Delta Urban Agglomeration: An Analysis Based on the Spatial Durbin Model. Sustainability, 16(2), 738. DOI: 10.3390/su16020738

Dezhina, I.G. (2014) Advanced Production Technologies: Russia's Place. Jekonomicheskoe razvitie Rossii = Economic development of Russia, 2, 47–50. (In Rus).

Demidova, O.A. (2021) Methods of Spatial Econometrics and Efficiency Estimation of State Programs. Prikladnaja jekonometrika = Applied econometrics, 64, 107–134. (In Rus.) DOI: 10.22394/1993-7601-2021-64-107-134.

Denisjuk, V.A., Markov, A.V. (2008) On the Formation of the Market of Advanced Manufacturing Technologies in CIS. Innovacii = Innovation, 7, 11–17. (In Rus.)

Fauzi, F., Wenur, G., Wasono, R. (2023). Spatial Durbin Model of Unemployment Rate in Central Java. Parameter: Journal of Statistics, 3(1), 7-18. DOI: 10.22487/27765660.2023.v3.i1.16423

Finlkel, S. (1996). Causal Analysis with Panel Data Steven E. Finkel. Journal of the American Statistical Association, 9(443), 441. DOI: 10.2307/2291441

Hansen, L. (1982) Large Sample Properties of Generalized Methods of Moments Estimators. Econometrica, 50, 1029–1054. DOI: 10.2307/1912775

Hou, X., Gao, S., Li, Q., Kang, Y., Chen, N., Chen, K., Rao, J., Ellenberg, J. S., Patz, J. A. (2021) Intracounty modeling of COVID-19 Infection with Human Mobility: Assessing Spatial Heterogeneity with Business Traffic, Age, and Race. Proceedings of the National Academy of Sciences of the United States of America, 118, 2020524118. DOI: 10.1073/pnas.2020524118

Jakushev, N.O. (2021) Features of the Development of Advanced Production Technologies in Russia in the Framework of Technological Entrepreneurship. Voprosy territorial'nogo razvitija = Issues of territorial development, 4, 2–15. (In Rus.) DOI: 10.15838/tdi.2021.4.59.2

Kapitcyn, V.M., Gerasimenko, O.A., Andronova, L.N. (2017). Analysis of the State and Trends in the Use of Advanced Manufacturing Technologies in Russia. Studies on Russian Economic Development, 1, 87–97. DOI: 10.1134/S107570071701004X

Kasimova, T.M. (2020) Panel Data Models as a Tool for Analysis and Forecasting of Economic Indicators of Russian Regions. Fundamental'nye issledovanija = Fundamental research, 3, 48–53. (In Rus.) DOI: 10.17513/fr.42698

Kudrjakov, E. A. (2019) Dynamics of the Use of Advanced Production Technologies in the Innovation Economy of the Russian Federation. Skif = Skif, 5–2, 114–117

Li, S., Adelmann, A. (2022) Review of Time Series Forecasting Methods and Their Applications to Particle Accelerators. ArXiv, 2209.10705. DOI: 10.48550/arXiv.2209.10705

Maddala, G. (1987) Limited Dependent Variable Models Using Panel Data. The Journal of Human Resources, 22(3), 307-338. DOI: 10.2307/145742

Mamleeva, Je.R., Trofimova, N.V., Sazykina, M.Ju. (2021) Development and Use of Advanced Production Technologies in the Russian Federation. Vestnik UGNTU. Nauka, obrazovanie, jekonomika. Serija: Jekonomika = Bulletin of USPTU. Science, education, economics. Series: Economics, 1, 8–14. (In Rus.) DOI: 10.17122/2541-8904-2021-1-35-8-14

Miller, M.A. (2015) Development and Use of Advanced Production Technologies in the Russian Industry. Vestnik Sibirskoj gosudarstvennoj avtomobil'no-dorozhnoj akademii = The Russian Automobile and Highway Industry Journal, 6, 112–119. (In Rus.)

Naumov, I.V., Otmahova, Ju.S., Krasnykh, S.S. (2021) A Methodological Approach to Modelling and Forecasting the Impact of Spatial Heterogeneity of COVID-19 Distribution Processes on Economic Development of Russian Regions. Komp'juternye issledovanija i modelirovanie = Computer research and modelling, 3, 629–648. (In Rus.) DOI: 10.20537/2076-7633-2021-13-3-629-648

Poljanskaja, E.S. (2022) Influence of Innovative Activity on Regional Level of Competitiveness of the Organisations. Finansovye rynki i banki = Financial markets and banks, 5, 40–46. (In Rus.)

Sarafidis, V., Wansbeek T. (2012). Cross-Sectional Dependence in Panel Data Analysis. Econometric Reviews, 31(5), 483-531, DOI: 10.1080/07474938.2011.611458

Sun, Z., Zhang, Y., Li, Y., Shao, X., Wang, C., Ye, Z. (2022). Research on Spatial Durbin Model for Highway Trip Generation by Intelligent Traffic Volume Forecast System. 2022 IEEE 2nd International Conference on Power, Electronics and Computer Applications (ICPECA), 803–806. DOI: 10.1109/ICPECA53709.2022.9719219

Varlamova, J, Kadochnikova, E. (2023). Modeling the Spatial Effects of Digital Data Economy on Regional Economic Growth: SAR, SEM and SAC Models. Mathematics, 11(16), 3516. DOI: 10.3390/math11163516

Zhang, B., Zhang, P. (2023). The construction and development of economic education model in universities based on the spatial Durbin model. Nonlinear Engineering, 12(1), 20220317. https://doi.org/10.1515/nleng-2022-0317

Zinov'eva, A.A., Rostova E.P. (2021) Analysis of Risk Factors Hindering the Implementation of Advanced Manufacturing Technologies. Cifrovye modeli i reshenija = Digital models and solutions, 1, 39–49. (In Rus.) DOI: 10.29141/2782-4934-2022-1-1-5




DOI: https://doi.org/10.15826/recon.2024.10.1.001

Copyright (c) 2024 Ilya V. Naumov, Sergey S. Krasnykh

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Online ISSN 2412-0731