У дослідженні розглядається роль ЗМІ у формуванні інфляційних очікувань серед різних респондентів в Україні. На основі великої сукупності новин із використанням методів машинного навчання отримано відповідні виміри. Вони подають кількісні показники для текстів, які визначають, чи теми новин відповідають інфляційним очікуванням. Дослідженням виявлено докази того, що різні теми новин можуть впливати на інфляційні очікування та здатні пояснити частину їх розбіжності. Результати дослідження можуть допомогти під час аналізу інфляційних очікувань, що є цінним, ураховуючи, що закріплення інфляційних очікувань залишається ключовим завданням для центральних банків.
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 https://www.bancaditalia.it/pubblicazioni/temidiscussione/2021/2021-1318/en_tema_1318.pdf
Azqueta-Gavaldón, A. (2017). Developing news-based Economic Policy Uncertainty index with unsupervised machine learning. Economics Letters, 158, 47–50. https://doi.org/10.1016/j.econlet.2017.06.032
Baker, S. R., Bloom, N., Davis, S. J. (2016). Measuring economic policy uncertainty. The Quarterly Journal of Economics, 131(4), 1593–1636. https://doi.org/10.1093/qje/qjw024
Bauer, M. D. (2015). Inflation expectations and the news. International Journal of Central Banking, 11(2), 1-40. Retrieved from https://www.ijcb.org/journal/ijcb15q2a1.pdf
Blei, D. M., Ng, A. Y., Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022. Retrieved from https://dl.acm.org/doi/10.5555/944919.944937
Carroll, C. D. (2003). Macroeconomic expectations of households and professional forecasters. The Quarterly Journal of Economics, 118(1), 269–298. https://doi.org/10.1162/00335530360535207
Coibion, O., Gorodnichenko, Y. (2012). What can survey forecasts tell us about information rigidities? Journal of Political Economy, 120(1), 116–159. https://doi.org/10.1086/665662
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. https://doi.org/10.1257/aer.20110306
Coibion, O., Gorodnichenko, Y. (2015b). Inflation expectations in Ukraine: A long path to anchoring? Visnyk of the National Bank of Ukraine, 233, 6–23. https://doi.org/10.26531/vnbu2015.233.006
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. https://doi.org/10.3386/w25482
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 https://www.cesifo.org/en/publications/2019/working-paper/exposure-daily-price-changes-and-inflation-expectations
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. https://doi.org/10.1177/0093650217750971
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. http://doi.org/10.18653/v1/N19-1423
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. https://doi.org/10.1111/obes.12189
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. https://doi.org/10.5089/9781484363010.001
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. https://doi.org/10.1177/107769900308000106
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. https://doi.org/10.1609/icwsm.v8i1.14550
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 https://cepr.org/publications/dp15168
King, G. (1986). How not to lie with statistics: avoiding common mistakes in quantitative political science. American Journal of Political Science, 30, 666. https://doi.org/10.2307/2111095
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. https://doi.org/10.1007/978-3-319-26123-2_31
Larsen, V. H., Thorsrud, L. A., Zhulanova, J. (2021). Newsdriven inflation expectations and information rigidities. Journal of Monetary Economics, 117, 507–520. https://doi.org/10.1016/j.jmoneco.2020.03.004
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. https://doi.org/10.1016/j.jedc.2009.09.004
Maćkowiak, B., Wiederholt, M. (2009). Optimal sticky prices under rational inattention. The American Economic Review, 99 (3), 769-803. https://doi.org/10.1257/aer.99.3.769
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. https://doi.org/10.1086/ma.18.3585256
Mazumder, S. (2021). The reaction of inflation forecasts to news about the Fed. Economic Modelling, 94, 256–264. https://doi.org/10.1016/j.econmod.2020.09.026
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. https://doi.org/10.1016/j.najef.2017.02.002
Pfajfar, D., Santoro, E. (2013). News on inflation and the epidemiology of inflation expectations. Journal of Money, Credit and Banking, 45(6), 1045–1067. https://doi.org/10.1111/jmcb.12043
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. https://doi.org/10.1162/tacl_a_00251
Sims, C. (2009). Inflation expectations, uncertainty and monetary policy. BIS Working Papers, 275. Basel: Bank for International Settlements. Retrieved from https://www.bis.org/publ/work275.htm
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. https://doi.org/10.1073/pnas.1908369116
Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M. (2011). Lexicon-based methods for sentiment analysis. Computational Linguistics, 37(2), 267–307. https://doi.org/10.1162/COLI_a_00049
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. https://doi.org/10.1016/j.ijforecast.2016.08.006
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 https://files.stlouisfed.org/files/htdocs/publications/review/04/07/Woodford.pdf
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. https://doi.org/10.1145/2736277.2741115
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. https://doi.org/10.26531/vnbu2019.247.02
APPENDIX E. DICTIONARY-BASED APPROACH
APPENDIX F. LDA APPROACH