Савенков Л.Д. Статистический анализ влияния макроэкономических факторов на мировое производство стали

Научная статья
УДК 311
https://doi.org/10.24158/pep.2025.10.31

Статистический анализ влияния макроэкономических факторов

на мировое производство стали

 
Леонид Дмитриевич Савенков
Тольяттинский государственный университет, Тольятти, Россия,
leonidsavenkov89@yandex.ru, https://orcid.org/0009-0002-4226-5165
 
Аннотация. В статье представлены результаты статистического анализа влияния макроэкономических факторов на мировое производство стали, проведенного на основе тестов причинности Грейнджера и анализа динамики временных рядов, чтобы определить наличие устойчивых экономических зависимостей. Результаты исследования показали, что, несмотря на существующую корреляцию между производством стали и макроэкономическими переменными, статистически значимой причинной связи между этими показателями в долгосрочной перспективе нет. Исследование подтверждает гипотезу о снижении роли сырьевых отраслей в экономическом росте в условиях глобализации и перехода к постиндустриальной экономике. Делается вывод о необходимости пересмотра подходов к анализу взаимосвязей между традиционными и новыми экономическими секторами в условиях быстро меняющейся глобальной экономики. Полученные результаты теста Грейнджера и анализа стационарности позволяют автору заключить, что между объемом производства стали и показателями долей импорта и экспорта руд и металлов не существует значимой причинно-следственной связи.
Ключевые слова: производство стали, ВВП, временные ряды, тест Грейнджера, стационарность
Финансирование: инициативная работа.
Для цитирования: Савенков Л.Д. Статистический анализ влияния макроэкономических факторов на мировое производство стали // Общество: политика, экономика, право. 2025. № 10. С. 261–267. https://doi.org/10.24158/pep.2025.10.31.
 
Original article
 

Statistical Analysis of the Impact of Macroeconomic Factors

on Global Steel Production

 
Leonid D. Savenkov
Tolyatti State University, Tolyatti, Russia,
leonidsavenkov89@yandex.ru, https://orcid.org/0009-0002-4226-5165
 
Abstract. This article presents a statistical analysis of the influence of macroeconomic factors on global steel production, conducted using Granger causality tests and time series analysis to determine the presence of stable economic relationships. The study’s findings demonstrate that, despite the existing correlation between steel production and macroeconomic variables, there is no statistically significant causal relationship between these indicators in the long term. The study confirms the hypothesis of a declining role of raw materials industries in economic growth in the context of globalization and the transition to a post-industrial economy. The article concludes that it is necessary to revise approaches to analyzing the relationship between traditional and new economic sectors in a rapidly changing global economy. The results of the Granger causality test and stationarity analysis indicate that there is no significant causal relationship between steel production volume and the shares of imports and exports of ores and metals.
Keywords: steel production, GDP, time series, Granger causality test, stationarity
Funding: Independent work.
For citation: Savenkov, L.D. (2025) Statistical Analysis of the Impact of Macroeconomic Factors on Global Steel Production. Society: Politics, Economics, Law. (10), 261–267. Available from: doi:10.24158/pep.2025.10.31  (In Russian).

© Савенков Л.Д., 2025

Список источников:
 
Acemoglu D. Introduction to Modern Economic Growth. Princeton, 2009. 1248 p.
Ai D., Li X., Liu G., Liang X., Xia L.C. Constructing the Microbial Association Network from Large-Scale Time Series Data Using Granger Causality // Genes. 2019. Vol. 10, iss. 3. P. 216. https://doi.org/10.3390/genes10030216.
Brynjolfsson E., McAfee A. The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. N. Y., 2014. 306 p.
Button K., Yuan J. Airfreight Transport and Economic Development: An Examination of Causality // Urban Studies. 2012. Vol. 50, iss. 2. P. 329–340. https://doi.org/10.1177/0042098012446999.
Gajdzik B., Wolniak R., Grebski W. An Econometric Model of the Operation of the Steel Industry in Poland in the Context of Process Heat and Energy Consumption // Energies. 2022. Vol. 15, iss. 21. P. 7909. https://doi.org/10.3390/en15217909.
He F., Wang Ch., Fan Sh.-K.S. Detection and Root Cause Analysis of a Batch Process via Novel Nonlinear Dissimilarity and Comparative Granger Causality Analysis // Industrial & Engineering Chemistry Research. 2019. Vol. 58, iss. 47. P. 21842–21854. https://doi.org/10.1021/acs.iecr.9b04471.
Lv F., Si Sh., Xiao X., Ren W. Modified Local Granger Causality Analysis Based on Peter‐Clark Algorithm for Multivariate Time Series Prediction on IoT Data // Computational Intelligence. 2024. Vol. 40, iss. 5. P. e12694. https://doi.org/10.1111/coin.12694.
Marinazzo D., Pellicoro M., Stramaglia S. Kernel Method for Nonlinear Granger Causality // Physical Review Letters. 2008. Vol. 100, iss. 14. P. 144103. https://doi.org/10.1103/physrevlett.100.144103.
Paul P., Mitra P. Revisiting Economic Growth and Steel Consumption: Evidence from India // The Indian Economic Journal. 2022. Vol. 72, iss. 3. P. 427–441. https://doi.org/10.1177/00194662221137264.
Schmidt C., Pester B., Schmid‐Hertel N., Witte H., Wismüller A., Leistritz L. A Multivariate Granger Causality Concept Towards Full Brain Functional Connectivity // Plos One. 2016. Vol. 11, iss. 4. P. e0153105. https://doi.org/10.1371/journal.pone.0153105.
Seth A., Barrett A., Barnett L. Granger Causality Analysis in Neuroscience and Neuroimaging // Journal of Neuroscience. 2015. Vol. 35, iss. 8. P. 3293–3297. https://doi.org/10.1523/jneurosci.4399-14.2015.
Stramaglia S., Scagliarini T., Antonacci Y., Faes L. Local Granger Causality // Physical Review E. 2021. Vol. 103, iss. 2. P. L020102. https://doi.org/10.1103/physreve.103.l020102.
Wei H., An J., Shen H., Zeng L.-L., Qiu Sh., Hu D. Altered Effective Connectivity Among Core Neurocognitive Networks  in Idiopathic Generalized Epilepsy: An fMRI Evidence // Frontiers in Human Neuroscience. 2016. Vol. 10. Р. 1–15. https://doi.org/10.3389/fnhum.2016.00447.
Wismüller A., DSouza A., Vosoughi M., Abidin A. Large-Scale Nonlinear Granger Causality for Inferring Directed Dependence from Short Multivariate Time-Series Data // Scientific Reports. 2021. Vol. 11, iss. 1. P. 7817. https://doi.org/10.1038/s41598-021-87316-6.

References:
 
Acemoglu, D. (2009) Introduction to Modern Economic Growth. Princeton. 1248 p.
Ai, D., Li, X., Liu, G., Liang, X. & Xia, L. C. (2019) Constructing the Microbial Association Network from Large-Scale Time Series Data Using Granger Causality. Genes. 10 (3), 216. Available from: doi:10.3390/genes10030216.
Brynjolfsson, E. & McAfee, A. (2014) The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. N. Y., 2014. 306 p.
Button, K. & Yuan, J. (2012) Airfreight Transport and Economic Development: An Examination of Causality. Urban Studies. 50 (2), 329–340. Available from: doi:10.1177/0042098012446999.
Gajdzik, B., Wolniak, R. & Grebski, W. (2022) An Econometric Model of the Operation of the Steel Industry in Poland in the Context of Process Heat and Energy Consumption. Energies. 15 (21), 7909. Available from: doi:10.3390/en15217909.
He, F., Wang, Ch. & Fan, Sh.-K.S. Detection and Root Cause Analysis of a Batch Process via Novel Nonlinear Dissimilarity and Comparative Granger Causality Analysis. Industrial & Engineering Chemistry Research. 58 (47), 21842–21854. Available from: doi:10.1021/acs.iecr.9b04471.
Lv, F., Si, Sh., Xiao, X. & Ren, W. (2024) Modified Local Granger Causality Analysis Based on Peter‐Clark Algorithm for Multivariate Time Series Prediction on IoT Data. Computational Intelligence. 40 (5), e12694. Available from: doi:10.1111/coin.12694.
Marinazzo, D., Pellicoro, M. & Stramaglia, S. (2008) Kernel Method for Nonlinear Granger Causality. Physical Review Letters. 100 (14), 144103. Available from: doi:10.1103/physrevlett.100.144103.
Paul, P. & Mitra, P. (2022) Revisiting Economic Growth and Steel Consumption: Evidence from India. The Indian Economic Journal. 72 (3), 427–441. Available from: doi:10.1177/00194662221137264.
Schmidt, C., Pester, B., Schmid‐Hertel, N., Witte, H., Wismüller, A. & Leistritz, L. A Multivariate Granger Causality Concept Towards Full Brain Functional Connectivity. Plos One. 11 (4), e0153105. Available from: doi:10.1371/journal.pone.0153105
Seth, A., Barrett, A. & Barnett, L. (2015) Granger Causality Analysis in Neuroscience and Neuroimaging. Journal of Neuroscience. 35 (8), 3293–3297. Available from: doi:10.1523/jneurosci.4399-14.2015
Stramaglia, S., Scagliarini, T., Antonacci, Y. & Faes, L. (2021) Local Granger Causality. Physical Review E. 103 (2), L020102. Available from: doi:10.1103/physreve.103.l020102.
Wei, H., An, J., Shen, H., Zeng, L.-L., Qiu, Sh. & Hu, D. (2016) Altered Effective Connectivity Among Core Neurocognitive Networks in Idiopathic Generalized Epilepsy: An fMRI Evidence. Frontiers in Human Neuroscience. 10, 1–15. Available from: doi:10.3389/fnhum.2016.00447.
Wismüller, A., Dsouza, A., Vosoughi, M. & Abidin, A. (2021) Large-Scale Nonlinear Granger Causality for Inferring Directed Dependence from Short Multivariate Time-Series Data. Scientific Reports. 11 (1), 7817. Available from: doi:10.1038/s41598-021-87316-6.