Inflation Forecasting with Monetary Policy Narratives: Evidence from an Emerging Economy
References
- Allard, M. A., Teiletche, P., & Zinebi, A. (2024). Enhancing inflation nowcasting with LLM: Sentiment analysis on news. arXiv preprint arXiv:2410.20198.
- Araujo, D., Borovicka, J., & Gopinath, G. (2025). Word2Prices: Embedding central bank communication for inflation prediction (BIS Working Papers No. 1253). Bank for International Settlements.
- Araci, D., (2019). Finbert: Financial sentiment analysis with pre-trained language models. arXiv preprint arXiv:1908.10063.
- Bańbura, M., Giannone, D., Modugno, M., & Reichlin, L. (2013). Now-casting and the real-time data flow. In Handbook of economic forecasting (Vol. 2, pp. 195-237). Elsevier.
- Bholat, D., Hansen, S., Santos, P., & Schonhardt-Bailey, C. (2015). Text mining for central banks. Centre for Central Banking Studies Handbook No. 33. Bank of England.
- Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of machine Learning research.
- Boukus, E., & Rosenberg, J. V. (2006). The information content of FOMC minutes (Staff Report No. 278). Federal Reserve Bank of New York.
- Clements, M. P., & Reade, J. J. (2020). Forecasting and forecast narratives: The Bank of England inflation reports. International Journal of Forecasting, 36(4), 1488-1500.
- de Jesus, D. P., & da Nóbrega Besarria, C. (2025). Central Bank Narratives and Macroeconomic Forecasting: Using Textual Analysis from Machine Learning: D. Pitta de Jesus et al. IMF Economic Review, 1-56.
- Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of NAACL-HLT, 4171–4186.
- Faria-e-Castro, M., & Leibovici, F. (2024). Artificial Intelligence and Inflation Forecasts. Federal Reserve Bank of St. Louis Review, 106(12), 1-14.
- Ferreira, L. N. (2021). Forecasting with VAR-teXt and DFM-teXt Models: exploring the predictive power of central bank communication. Banco Central do Brasil.
- Gentzkow, M., Kelly, B., & Taddy, M. (2019). Text as data. Journal of Economic Literature, 57(3), 535–574.
- Giannone, D., Lenza, M., & Primiceri, G. E. (2015). Prior selection for vector autoregressions. Review of Economics and Statistics, 97(2), 436-451.
- Hansen, S., & McMahon, M. (2016). Shocking language: Understanding the macroeconomic effects of central bank communication. Journal of International Economics, 99, S114-S133.
- Hansen, S., McMahon, M., & Tong, M. (2019). The long-run information effect of central bank communication. Journal of Monetary Economics, 109, 1–19.
- Marcucci, J. (2024). Macroeconomic forecasting with text-based data. Handbook of Research Methods and Applications in Macroeconomic Forecasting, 425-468.
- Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.
- Niţoi, M., Pochea, M.-M., & Radu, Ş.-C. (2023). Unveiling the sentiment behind central bank narratives: A novel deep learning index. Journal of Behavioral and Experimental Finance, 38, 100809. https://doi.org/10.1016/j.jbef.2023.100809
- Simionescu, M. (2025). Machine learning vs. econometric models to forecast inflation rate in Romania? The role of sentiment analysis. Mathematics, 13(1), 168.
- Wang, R., Sun, M., & Wang, L. (2025). From news to trends: A financial time series forecasting framework with LLM-driven news sentiment analysis and selective state spaces. Journal of Intelligent Information Systems, 63(6), 1955–1980. https://doi.org/10.1007/s10844-025- 00971-3
- Zarifhonarvar, A. (2025). Generating inflation expectations with large language models. Journal of Monetary Economics, 103859.
DOI: https://doi.org/10.2478/picbe-2026-0012 | Journal eISSN: 2558-9652
Language: English
Page range: 75 - 83
Published on: Jul 15, 2026
Published by: Bucharest University of Economic Studies
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
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© 2026 Antoaneta AMZA, Vlad BOLOVĂNEANU, Daniel Traian PELE, published by Bucharest University of Economic Studies
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.