The Role of the Media in the Inflation Expectation Formation Process
a National Bank of Ukraine, Kyiv, Ukraine

This research highlights the role played by the media in the formation of inflation expectations among various respondents in Ukraine. Using a large news corpus and machine-learning techniques, I have constructed newsbased metrics that produce quantitative indicators for texts, which show if the news topics are relevant to inflation expectations. I have found evidence that various news topics may have an impact on inflation expectations, and can explain part of their variance. Thus, my results could help in the analysis of inflation expectations – which is of value, given that anchoring inflation expectations remains a key challenge for central banks.

Full Text
Cite as: Yukhymenko, T. (2022). The Role of the Media in the Inflation Expectation Formation Process. Visnyk of the National Bank of Ukraine, 253, 4-26.
Citation Format


Angelico, C., Marcucci, J., Miccoli, M., Quarta, F. (2021). Can we measure inflation expectations using Twitter? Bank of Italy Working Papers, 1318. Rome: Bank of Italy. Retrieved from

Azqueta-Gavaldón, A. (2017). Developing news-based Economic Policy Uncertainty index with unsupervised machine learning. Economics Letters, 158, 47–50.

Baker, S. R., Bloom, N., Davis, S. J. (2016). Measuring economic policy uncertainty. The Quarterly Journal of Economics, 131(4), 1593–1636.

Bauer, M. D. (2015). Inflation expectations and the news. International Journal of Central Banking, 11(2), 1-40. Retrieved from

Blei, D. M., Ng, A. Y., Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022. Retrieved from

Carroll, C. D. (2003). Macroeconomic expectations of households and professional forecasters. The Quarterly Journal of Economics, 118(1), 269–298.

Coibion, O., Gorodnichenko, Y. (2012). What can survey forecasts tell us about information rigidities? Journal of Political Economy, 120(1), 116–159.

Coibion, O., Gorodnichenko, Y. (2015a). Information rigidity and the expectations formation process: A simple framework and new facts. American Economic Review, 105(8), 2644–2678.

Coibion, O., Gorodnichenko, Y. (2015b). Inflation expectations in Ukraine: A long path to anchoring? Visnyk of the National Bank of Ukraine, 233, 6–23.

Coibion, O., Gorodnichenko, Y., Weber, M. (2019). Monetary Policy Communications and their Effects on Household Inflation Expectations. NBER Working Paper Series, 25482. Cambridge: National Bureau of Economic Research.

D'Acunto, F., Malmendier, U., Ospina, J., Weber, M. (2019). Exposure to daily price changes and inflation expectations. CESifo Working Paper, 7798. Munich: CESifo. Retrieved from

Damstra, A., Boukes, M. (2018). The economy, the news, and the public: A longitudinal study of the impact of economic news on economic evaluations and expectations. Communication Research, 48(1), 26–50.

Devlin, J., Chang, M.-W., Lee, K., Toutanova, K. (2018). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 1, 4171–4186.

Dräger, L. Lamla, M. J. (2017). Imperfect information and consumer inflation expectations: Evidence from microdata. Oxford Bulletin of Economics and Statistics, 79(6), 933–968.

Galati, G., Heemeijer, P., Moessner, R. (2011), How do inflation expectations form? New insights from a highfrequency survey. BIS Working Papers, 349. Basel: Bank for International Settlements.

Garcia, J. A. Werner, S. (2018). Inflation news and Euro area inflation expectations. IMF Working Papers, 167. Washington: International Monetary Fund.

Hester, J. B., Gibson, R. (2003). The economy and secondlevel agenda setting: A Time-series analysis of economic news and public opinion about the economy. Journalism & Mass Communication Quarterly, 80(1), 73–90.

Hutto, C .J., Gilbert, E. (2014). VADER: A parsimonious rule-based M\model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media, 8(1), 216–225.

Kilian, L., Zhou, X. (2020). Oil prices, gasoline prices and inflation expectations: A new model and new facts. CEPR Discussion Paper, 15168. London: Centre for Economic Policy Research. Retrieved from

King, G. (1986). How not to lie with statistics: avoiding common mistakes in quantitative political science. American Journal of Political Science, 30, 666.

Korobov, M. (2015). Morphological analyzer and generator for russian and Ukrainian languages. In: Khachay, M., Konstantinova, N., Panchenko, A., Ignatov, D., Labunets, V. (eds) Analysis of Images, Social Networks and Texts. AIST 2015. Communications in Computer and Information Science, 542. Springer.

Larsen, V. H., Thorsrud, L. A., Zhulanova, J. (2021). Newsdriven inflation expectations and information rigidities. Journal of Monetary Economics, 117, 507–520.

Lines, M., Westerhoff, F. H. (2010). Inflation expectations and macroeconomic dynamics: the case of rational versus extrapolative expectations. Journal of Economic Dynamics and Control, 34(2), 246–257.

Maćkowiak, B., Wiederholt, M. (2009). Optimal sticky prices under rational inattention. The American Economic Review, 99 (3), 769-803.

Mankiw, N. G., Reis, R.F., Wolfers, J. (2003). Disagreement about inflation expectations. NBER Macroeconomics Annual, 18, 209–248. Cambridge: National Bureau of Economic Research.

Mazumder, S. (2021). The reaction of inflation forecasts to news about the Fed. Economic Modelling, 94, 256–264.

Nautz, D., Pagenhardt, L., Strohsal, T. (2017). The (de-)anchoring of inflation expectations: New evidence from the euro area. The North American Journal of Economics and Finance, 40, 103–115.

Pfajfar, D., Santoro, E. (2013). News on inflation and the epidemiology of inflation expectations. Journal of Money, Credit and Banking, 45(6), 1045–1067.

Rozovskaya, A., Roth, D. (2019). Grammar error correction in morphologically rich languages: The case of russian. Transactions of the Association for Computational Linguistics, 7, 1–17.

Sims, C. (2009). Inflation expectations, uncertainty and monetary policy. BIS Working Papers, 275. Basel: Bank for International Settlements. Retrieved from

Soroka, S., Fournier, P., Nir, L. (2019). Cross-national evidence of a negativity bias in psychophysiological reactions to news. Proceedings of the National Academy of Sciences of the United States of America, 116(38), 18888–18892.

Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M. (2011). Lexicon-based methods for sentiment analysis. Computational Linguistics, 37(2), 267–307.

Tobback, E., Naudts, H., Daelemans, W., Junqué de Fortuny, E., Martens, D. (2016). Belgian economic policy uncertainty index: Improvement through text mining. International Journal of Forecasting, 34(2), 355–365.

Woodford, M. (2004). Inflation targeting and optimal monetary policy. Federal Reserve Bank of St. Louis Review, 86(4), 15-41. St. Louis: The Federal Reserve Bank of St. Louis. Retrieved from

Yuan, J., Gao, F., Ho, Q., Dai, W., Wei, J., Zheng, X., Xing, E.P., Liu, T., Ma, W. (2015). LightLDA: Big topic models on modest computer clusters. WWW’15: Proceedings of the 24th International Conference on World Wide Web, 1351–1361.

Zholud, O., Lepushynskyi, V., Nikolaychuk, S. (2019). The Effectiveness of the Monetary Transmission Mechanism in Ukraine since the Transition to Inflation Targeting. Visnyk of the National Bank of Ukraine, 247, 19–37.

Supplementary Materials



Rights and Permissions
This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material.
Submit Your Paper