A SYSTEMATIC ANALYSIS OF THE APPLICATION OF DYNAMIC ECONOMETRIC MODELS IN ECONOMICS
DOI:
https://doi.org/10.60078/3060-4842-2025-vol2-iss4-pp344-351Abstract
This article analyzes the application of dynamic econometric models in economics, particularly the contemporary trends observed in recent years. These models (for example, DSGE, VAR, and dynamic panel models) serve as primary tools for evaluating economic dynamics, shock impacts, and policy assessment, with their precision being enhanced through the integration of machine learning (ML) and big data. The methodology employs a systematic literature review, involving the analysis of several research studies from prestigious journals. The results demonstrate that DSGE models provide macroeconomic forecasts with RMSE values ranging from 0.15 to 0.25, VAR models assess shock impacts, and ML integration improves accuracy by 20-25%, although limitations exist due to computational complexity and data uncertainty. In conclusion, the role of these models in shaping economic policy is emphasized, with recommendations provided for deepening ML integration, expanding the utilization of big data, and addressing the aforementioned constraints.
Keywords:
dynamic econometric models economic applications DSGE models VAR models machine learning integration macroeconomic forecasting shock impacts modern trendsReferences
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.
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