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ОСОБЕННОСТИ ЭКОНОМЕТРИЧЕСКОГО МОДЕЛИРОВАНИЯ В СОВРЕМЕННОЙ ЭКОНОМИКЕ

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Iqtisodiy taraqqiyot va tahlil

Iqtisodiy taraqqiyot va tahlil

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АННОТАЦИЯ СТАТЬИ

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Данная статья исследует специфические аспекты эконометрического моделирования в динамичных и сложных условиях современной экономики. В работе подчеркиваются современные тенденции, такие как интеграция больших данных (big data), машинного обучения (machine learning) и искусственного интеллекта, которые играют ключевую роль в прогнозировании влияния инфляции, безработицы, изменения климата и пандемий. В методологии применяется систематический анализ литературы, опирающийся на научные статьи из баз данных Scopus, Web of Science и ResearchGate за последние пять лет (2020-2025). Результаты показывают, что гибридные модели на основе ML повышают точность прогнозирования (снижение RMSE и MAE), однако негативные эффекты изменения климата и неопределенность данных создают значительные трудности. В выводах и рекомендациях предлагается повышение устойчивости моделей в формировании политики, усиление междисциплинарного сотрудничества и внедрение этических стандартов, что способствует устойчивому развитию и экономическому восстановлению.

АВТОРЫ

A.Rajabov

Ma’mun Universiteti

Теги

# inflation forecasting# большие данные# Big Data# climate change# Машинное обучение# Machine Learning# современная экономика# zamonaviy iqtisodiyot# modern economy# ekonometrik modellashtirish# эконометрическое моделирование# econometric modeling# barqaror rivojlanish.# sustainable development.# iqlim oʻzgarishi# устойчивое развитие.# katta ma’lumotlar# mashinaviy oʻrganish# DSGE modellar# inflyatsiya prognozi# изменение климата# модели DSGE# прогнозирование инфляции# DSGE models

ДРУГИЕ СТАТЬИ ЭТОГО ЖУРНАЛА

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ROI:

https://eroi.uz/11.0183/A0126-0005

DOI:

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Список литературы

Ali, W., Ambiya, & Dash, D. P. (2023). Examining the Perspectives of Gender Development and Inequality: A Tale of Selected Asian Economies. Administrative Sciences, 13(4), 115. https://doi.org/10.3390/admsci13040115

Alomani, G., et al. (2025). Global inflation forecasting and Uncertainty Assessment: Comparing ARIMA with advanced machine learning. Journal of Radiation Research and Applied Sciences, 18(2), 101402. https://doi.org/10.1016/j.jrras.2025.101402

Chudo, S. B., & Terdik, G. (2025). Modeling and Forecasting Time-Series Data with Multiple Seasonal Periods Using Periodograms. Econometrics, 13(2), 14. https://doi.org/10.3390/econometrics13020014

Chung, D., & Hwang, J. (2022). An Economic and Social Impact of International Aid at National Level: application of spatial panel model. World, 3(3), 575-585. https://doi.org/10.3390/world3030031

De Zarzà, I., et al. (2023). Optimized financial planning: integrating individual and cooperative models with LLM recommendations. AI, 5(1), 91-114. https://doi.org/10.3390/ai5010006

Dritsaki, M., & Dritsaki, C. (2023). R&D Expenditures on innovation: A panel cointegration study of the EU Countries. Sustainability, 15(8), 6637. https://doi.org/10.3390/su15086637

Fu, R., Deng, D., & Liu, T. (2023). The Impact of Aging on Housing Market: Evidence from China. Sustainability, 15(5), 4161. https://doi.org/10.3390/su15054161

Furmankiewicz, M., et al. (2021). Climate change challenges and community-led development strategies: Do they fit together in fisheries regions?. Energies, 14(20), 6614. https://doi.org/10.3390/en14206614

Gričar, S., Lojanica, N., & Backović, T. (2025). Financial Econometrics and Quantitative Economic Analysis. Journal of Risk and Financial Management, 18(3), 166. https://doi.org/10.3390/jrfm18030166

Jo, C., Kim, D. H., & Lee, J. W. (2023). Forecasting unemployment and employment: A system dynamics approach. Technological Forecasting and Social Change, 194, 122715. https://doi.org/10.1016/j.techfore.2023.122715

Khan, R. Z., Razak, L. A., & Premaratne, G. (2025). Green Growth and Sustainability: A Systematic Literature Review on Theories, Measures and Future Directions. Cleaner and Responsible Consumption, 100274. https://doi.org/10.1016/j.clrc.2025.100274

Lakner, Z., et al. (2024). Possibilities and limits of modelling of long-range economic consequences of air pollution–A case study. Heliyon, 10(4). https://doi.org/10.1016/j.heliyon.2024.e26483

Majeed, A., et al. (2024). The symmetric effect of financial development, human capital and urbanization on ecological footprint: Insights from BRICST economies. Sustainability, 16(12), 5051. https://doi.org/10.3390/su16125051

McKibbin, W., & Fernando, R. (2023). The global economic impacts of the COVID-19 pandemic. Economic Modelling, 129, 106551. https://doi.org/10.1016/j.econmod.2023.106551

Ali, W., Ambiya, & Dash, D. P. (2023). Examining the Perspectives of Gender Development and Inequality: A Tale of Selected Asian Economies. Administrative Sciences, 13(4), 115. https://doi.org/10.3390/admsci13040115

Alomani, G., et al. (2025). Global inflation forecasting and Uncertainty Assessment: Comparing ARIMA with advanced machine learning. Journal of Radiation Research and Applied Sciences, 18(2), 101402. https://doi.org/10.1016/j.jrras.2025.101402

Chudo, S. B., & Terdik, G. (2025). Modeling and Forecasting Time-Series Data with Multiple Seasonal Periods Using Periodograms. Econometrics, 13(2), 14. https://doi.org/10.3390/econometrics13020014

Chung, D., & Hwang, J. (2022). An Economic and Social Impact of International Aid at National Level: application of spatial panel model. World, 3(3), 575-585. https://doi.org/10.3390/world3030031

De Zarzà, I., et al. (2023). Optimized financial planning: integrating individual and cooperative models with LLM recommendations. AI, 5(1), 91-114. https://doi.org/10.3390/ai5010006

Dritsaki, M., & Dritsaki, C. (2023). R&D Expenditures on innovation: A panel cointegration study of the EU Countries. Sustainability, 15(8), 6637. https://doi.org/10.3390/su15086637

Fu, R., Deng, D., & Liu, T. (2023). The Impact of Aging on Housing Market: Evidence from China. Sustainability, 15(5), 4161. https://doi.org/10.3390/su15054161

Furmankiewicz, M., et al. (2021). Climate change challenges and community-led development strategies: Do they fit together in fisheries regions?. Energies, 14(20), 6614. https://doi.org/10.3390/en14206614

Gričar, S., Lojanica, N., & Backović, T. (2025). Financial Econometrics and Quantitative Economic Analysis. Journal of Risk and Financial Management, 18(3), 166. https://doi.org/10.3390/jrfm18030166

Jo, C., Kim, D. H., & Lee, J. W. (2023). Forecasting unemployment and employment: A system dynamics approach. Technological Forecasting and Social Change, 194, 122715. https://doi.org/10.1016/j.techfore.2023.122715

Khan, R. Z., Razak, L. A., & Premaratne, G. (2025). Green Growth and Sustainability: A Systematic Literature Review on Theories, Measures and Future Directions. Cleaner and Responsible Consumption, 100274. https://doi.org/10.1016/j.clrc.2025.100274

Lakner, Z., et al. (2024). Possibilities and limits of modelling of long-range economic consequences of air pollution–A case study. Heliyon, 10(4). https://doi.org/10.1016/j.heliyon.2024.e26483

Majeed, A., et al. (2024). The symmetric effect of financial development, human capital and urbanization on ecological footprint: Insights from BRICST economies. Sustainability, 16(12), 5051. https://doi.org/10.3390/su16125051

McKibbin, W., & Fernando, R. (2023). The global economic impacts of the COVID-19 pandemic. Economic Modelling, 129, 106551. https://doi.org/10.1016/j.econmod.2023.106551