DINAMIK EKONOMETRIK MODELLARNING IQTISODIYOTDA QOʻLLANILISHI BOʻYICHA SISTEMATIK TAHLIL

Mualliflar

DOI:

https://doi.org/10.60078/3060-4842-2025-vol2-iss4-pp344-351

Annotasiya

Mazkur maqola dinamik ekonometrik modellarining iqtisodiyotdagi qoʻllanilishini, xususan, soʻnggi yillardagi zamonaviy tendensiyalarni tahlil qiladi. Ushbu modellar (masalan, DSGE, VAR va dinamik panel modellar) iqtisodiy dinamikani, shok ta’sirlarini va siyosatni baholashda asosiy vosita boʻlib, mashina oʻrganish (ML) va katta ma’lumotlar integratsiyasi orqali aniqligi oshirilmoqda. Metodologiyada sistematik adabiyotlar sharhi qoʻllanilgan boʻlib, nufuzli jurnallardan bir qancha tadqiqot ishlari tahlil etilgan. Natijalar DSGE modellarining RMSE 0,15-0,25 darajasida makroiqtisodiy prognozni, VAR modellarining shok ta’sirlarini va ML integratsiyasining aniqlikni 20-25% ga oshirishini koʻrsatdi, ammo hisoblash murakkabligi va ma’lumotlar noaniqligi cheklovlari mavjud. Xulosada modellarning iqtisodiy siyosatni shakllantirishdagi roli ta’kidlanib, ML integratsiyasini chuqurlashtirish, big data dan foydalanishni kengaytirish va cheklovlarni bartaraf etish boʻyicha takliflar berilgan.

Kalit so‘zlar:

dinamik ekonometrik modellar iqtisodiy qoʻllanish DSGE modellar VAR modellar mashina oʻrganish integratsiyasi makroiqtisodiy prognoz shok ta’sirlari zamonaviy tendensiyalar

Bibliografik manbalar

Albuquerque, D., Chan, J., Kanngiesser, D., Latto, D., Lloyd, S., Singh, S., & Žáček, J. (2025). Decompositions, forecasts and scenarios from an estimated DSGE model for the UK economy.

Altunöz, U. (2025). Deciphering the Role of Expectations in the Process of Inflation Formation in the USA. SAGE Open, 15(2), 21582440251335142. https://doi.org/10.1177/21582440251335142.

Antolin-Diaz, J., Drechsel, T., & Petrella, I. (2021). Advances in nowcasting economic activity: Secular trends, large shocks and new data. https://ssrn.com/abstract=3805349.

Beare, B. K., & Toda, A. A. (2022). Determination of Pareto exponents in economic models driven by Markov multiplicative processes. Econometrica, 90 (4), 1811-1833. https://doi.org/10.3982/ECTA17984.

Bismans, F.J., Damette, O. (2025). Dynamics in Econometrics. In: Dynamic Econometrics. Palgrave Macmillan, Cham., pp. 33-63, https://doi.org/10.1007/978-3-031-72910-2_2.

Bond, S.R. Dynamic panel data models: a guide to micro data methods and practice. Portuguese Economic Journal 1, 141–162 (2002). https://doi.org/10.1007/s10258-002-0009-9.

Brzoza-Brzezina, M., & Suda, J. (2021). Are DSGE models irreparably flawed? Bank i Kredyt, 52(3), 227-252. https://bac.nbp.pl/content/2021/03/BIK_03_2021_02.pdf.

Canova, F., & Ciccarelli, M. (2013). Panel Vector Autoregressive Models: A Survey☆ The views expressed in this article are those of the authors and do not necessarily reflect those of the ECB or the Eurosystem. In VAR models in macroeconomics–new developments and applications: Essays in honor of Christopher A. Sims (pp. 205-246). Emerald Group Publishing Limited. https://doi.org/10.1108/S0731-9053(2013)0000031006.

Čapek, J., Crespo Cuaresma, J., Chalmovianský, J., & Reichel, V. (2025). Real‐Time Data, Revisions and the Predictive Ability of DSGE Models. Oxford Bulletin of Economics and Statistics. https://doi.org/10.1111/obes.12677.

Carriero, A., Pettenuzzo, D., & Shekhar, S. (2024). Macroeconomic forecasting with large language models. arXiv preprint arXiv:2407.00890.

Catania, L. (2021). Dynamic adaptive mixture models with an application to volatility and risk. Journal of Financial Econometrics, 19(4), 531-564. https://doi.org/10.1093/jjfinec/nbz018.

Chudik, A., Mohaddes, K., Pesaran, M. H., Raissi, M., & Rebucci, A. (2021). A counterfactual economic analysis of Covid-19 using a threshold augmented multi-country model. Journal of International Money and Finance, 119, 102477. https://doi.org/10.1016/j.jimonfin.2021.102477.

Desai, A. (2023). Machine Learning for Economics Research: When What and How?. arXiv preprint arXiv:2304.00086. https://doi.org/10.34989/san-2023-16.

Diemer, A., & Sourgou, H. (2024). Modeling Economics and Sustainability: GDP as a Goal vs GDP as a Driver. iBusiness, 16(3), 101-138. https://doi.org/10.4236/ib.2024.163008.

Fernández-Villaverde, J., & Guerrón-Quintana, P. A. (2021). Estimating DSGE models: Recent advances and future challenges. Annual Review of Economics, 13(1), 229-252. https://doi.org/10.1146/annurev-economics-081020-044812.

Gelain, P., & Lopez, P. (2024). Understanding post-pandemic surprises in inflation and the labor market. Economic Commentary, (2024-11). https://doi.org/10.26509/frbc-ec-202411.

Giacomini, R. (2013). The relationship between DSGE and VAR models. VAR models in macroeconomics–new developments and applications: Essays in honor of Christopher A. Sims, 1-25. https://doi.org/10.1108/S0731-9053(2013)0000031001.

Iskhakov, F., Rust, J., & Schjerning, B. (2020). Machine learning and structural econometrics: contrasts and synergies. The Econometrics Journal, 23(3), S81-S124. https://doi.org/10.1093/ectj/utaa019.

Khan, A. M., & Wyrwa, A. (2025). Integrating Machine Learning and Econometric Models to Uncover Macroeconomic Determinants of Renewable Energy Production in the Selected European Countries. Energy, 137266. https://doi.org/10.1016/j.energy.2025.137266.

Khan, F., Iftikhar, H., Khan, I., Rodrigues, P. C., Alharbi, A. A., & Allohibi, J. (2025). A Hybrid Vector Autoregressive Model for Accurate Macroeconomic Forecasting: An Application to the US Economy. Mathematics, 13(11), 1706. https://doi.org/10.3390/math13111706.

Kilic, Rehim (2025). “Linear and nonlinear econometric models against machine learning models: realized volatility prediction,” Finance and Economics Discussion Series 2025-061. Washington: Board of Governors of the Federal Reserve System, https://doi.org/10.17016/FEDS.2025.061.

Lewis, D. J., Mertens, K., Stock, J. H., & Trivedi, M. (2022). Measuring real activity using a weekly economic index. Journal of Applied Econometrics, 37(4), 667-687. https://doi.org/10.1002/jae.2873.

Li, C., Jia, H., & Yang, B. (2020, August). Simulation Analysis of the Impact of the Rural Financial Efficiency on the Rural Economic Fluctuations. In International Conference on Simulation Tools and Techniques (pp. 371-384). Cham: Springer International Publishing. http://dx.doi.org/10.1007/978-3-030-72795-6_31.

Li, X., & Yuan, J. (2024). DeepTVAR: Deep learning for a time-varying VAR model with extension to integrated VAR. International Journal of Forecasting, 40(3), 1123-1133. https://doi.org/10.1016/j.ijforecast.2023.10.001.

Lin, C. H., Liu, T., & Vincent, K. (2025). Should Economic Theories Guide the Machine Learning Model in Forecasting Exchange Rate? Economic Modelling, 107224. https://doi.org/10.1016/j.econmod.2025.107224 .

Lucchetta, M. (2025). Crisis-Proofing Heterogeneous Banks. Department of Economics Research Paper Series, (08). https://dx.doi.org/10.2139/ssrn.5341815.

Maliar, L., Maliar, S., & Winant, P. (2021). Deep learning for solving dynamic economic models. Journal of Monetary Economics, 122, 76-101. https://doi.org/10.1016/j.jmoneco.2021.07.004.

Nymoen, R. (2023). Economic Covid-19 effects analysed by macro econometric models – the case of Norway. National Accounting Review, 5(1), 1-22.

Pettenuzzo, D., Sabbatucci, R., & Timmermann, A. (2023). Dividend suspensions and cash flows during the Covid-19 pandemic: A dynamic econometric model. Journal of econometrics, 235(2), 1522-1541. https://doi.org/10.1016/j.jeconom.2022.11.008.

Rajab, K., Kamalov, F. & Cherukuri, A.K. Forecasting COVID-19: Vector Autoregression-Based Model. Arab J Sci Eng 47, 6851–6860 (2022). https://doi.org/10.1007/s13369-021-06526-2.

Shi, C. (2025). From Econometrics to Machine Learning: Transforming Empirical Asset Pricing. Journal of Economic Surveys. https://dx.doi.org/10.2139/ssrn.5150205.

Srdelić, L. (2024). Climate macroeconomic modelling handbook. https://www.ngfs.net/system/files/import/ngfs/medias/documents/ngfs_climate-macroeconomic-modelling- handbook.pdf.

Supardi, B. (2024). The role of econometrics in predicting economic trends: Challenges and innovations. Journal of Economics and Economic Education Research, 25(S6), 1-3.

Yuklashlar

Nashr qilingan

Qanday qilib iqtibos keltirish kerak

Rajabov , A. (2025). DINAMIK EKONOMETRIK MODELLARNING IQTISODIYOTDA QOʻLLANILISHI BOʻYICHA SISTEMATIK TAHLIL. Ilgʻor Iqtisodiyot Va Pedagogik Texnologiyalar, 2(4), 344-351. https://doi.org/10.60078/3060-4842-2025-vol2-iss4-pp344-351