Article Sidebar
Main Article Content
This paper examines how artificial intelligence and machine learning have reshaped macroeconomic forecasting in the volatile post-COVID era. Highlighting the use of ensemble methods, neural networks, and large language models, it illustrates their advantages in capturing nonlinear dynamics and processing complex data. Drawing on central bank case studies, the paper shows that AI enhances predictive power, though interpretability and robustness remain challenges. AI is best seen as a complement to, not a replacement for, traditional economic models and human judgment.
Article Details
Aldasoro I., Hördahl P., Schrimpf A., Zhu X. S. (2025) Predicting Financial Market Stress with Machine Learning. BIS Working Papers 1250, BIS. (Crossref)
Almosova K. Andresen M. (2022) Non-linear Inflation Forecasting with Recurrent Neural Networks. Journal of Forecasting, 41(8), 1250-1269. https://doi.org/10.1002/for.2901 (Crossref)
Bareith H., Adjei E., Hassan M. Kokot W. (2024) Navigating Inflation Challenges: AI-Based Portfolio-Management Insights. Risks, 12(3), Art. 46. https://doi.org/10.3390/risks12030046 (Crossref)
Cárdenas-Cárdenas J. A., Cristiano-Botia D. J., Martínez-Cortés N. (2023) Colombian Inflation Forecast using Long Short-Term Memory Approach. Borradores de Economía, 1241. (Crossref)
Czech National Bank (2025) First Use of AI in Inflation Forecasting at the CNB. CNB Research Note.
Desai A. (2023) Machine Learning for Economics Research: When What and How? Staff Analytical Note 2023-16. Bank of Canada. (Crossref)
Faria-e-Castro M., & Leibovici F. (2024) Artificial Intelligence and Inflation Forecasts. Federal Reserve Bank of St. Louis Review, 106(12), 1-14. (Crossref)
IMF (2024) Mending the Crystal Ball: Enhanced Inflation Forecasts with Machine Learning. IMF Working Paper. (Crossref)
Joseph A., Potjagailo G., Chakraborty C., Kapetanios G. (2024) Forecasting UK Inflation bottom up. International Journal of Forecasting, 40(4), 1521-1538. (Crossref)
Jouilil M., Iaousse A. (2023) Comparing the Accuracy of Classical and Machine-Learning Methods in Time-Series Forecasting: a Case Study of USA Inflation. Statistics, Optimisation & Information Computing, 11(3), 1767-1784. https://doi.org/10.19139/soic-2310-5070-1767 (Crossref)
Lenza M., Moutachaker I., Paredes J. (2023a) Density Forecasts of Inflation: a Quantile Regression Forest Approach. European Central Bank, Working Paper Series, 2830. (Crossref)
Lenza M., Moutachaker I., Paredes J. (2023b) Forecasting Euro Area Inflation with Machine-Learning Models. European Central Bank, Research Bulletin, 112.
Li J. (2024) Inflation Forecasting Using a Hybrid LSTM-SARIMA Model Based on Discrete Wavelet Transform. Advances in Economics, Management and Political Sciences, 73, 1419-1434. https://doi.org/10.54254/2754-1169/73/20231419 (Crossref)
Liu Y. (2024) Forecasting China’s Consumer Price Index (CPI) based on Combined ARIMA-LSTM Models. Frontiers in Business Economics and Management, 3(2), 49-67. https://doi.org/10.54097/v49dwv67 (Crossref)
Lopez-Lira A., Tang Y. (2024) Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models, arXiv preprint, arXiv:2304.07619. (Crossref)
Mullainathan S., Spiess J. (2017) Machine Learning: An Applied Econometric Approach. Journal of Economic Perspectives, 31(2), 87-106. (Crossref)
Tchoketch-Kebir A. Madouri A. (2024) Research Leadership and High Standards in Economic Forecasting: Neural-Network Models Compared with Etalon ARIMA Models. Business Ethics and Leadership, 8(1), 220-233. https://doi.org/10.61093/bel.8(1).220-233.2024 (Crossref)
Downloads
Utwór dostępny jest na licencji Creative Commons Uznanie autorstwa – Użycie niekomercyjne 4.0 Międzynarodowe.
Publikowane artykuły dostępne są na warunkach Open Access na zasadach licencji Creative Commons CC BY-NC – do celów niekomercyjnych udostępnione materiały mogą być kopiowane, drukowane i rozpowszechniane. Autorzy ponoszą opłatę za opublikowanie artykułu.