IDEAS home Printed from https://ideas.repec.org/p/gii/giihei/heidwp13-2021.html
   My bibliography  Save this paper

Role of the Media in the Inflation Expectation Formation Process

Author

Listed:
  • Tetiana Yukhymenko

    (National Bank of Ukraine)

Abstract

This research highlights the role played by the media in the inflation expectations formation process of different types of respondents in Ukraine. Using a large news corpus and machine learning techniques I constructed news-based measures transforming text into quantitative indicators, which reflect news topics relevant to inflation expectations. As such, I found evidence that the different news topics have an impact on inflation expectations and can explain part of their variance. Thus, my results can help understand inflation expectations, especially as anchoring inflation expectations remains a key challenge for central banks.

Suggested Citation

  • Tetiana Yukhymenko, 2021. "Role of the Media in the Inflation Expectation Formation Process," IHEID Working Papers 13-2021, Economics Section, The Graduate Institute of International Studies.
  • Handle: RePEc:gii:giihei:heidwp13-2021
    as

    Download full text from publisher

    File URL: http://repec.graduateinstitute.ch/pdfs/Working_papers/HEIDWP13-2021.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Olivier Coibion & Yuriy Gorodnichenko, 2012. "What Can Survey Forecasts Tell Us about Information Rigidities?," Journal of Political Economy, University of Chicago Press, vol. 120(1), pages 116-159.
    2. Michael D. Bauer, 2015. "Inflation Expectations and the News," International Journal of Central Banking, International Journal of Central Banking, vol. 11(2), pages 1-40, March.
    3. Olivier Coibion & Yuriy Gorodnichenko, 2015. "Information Rigidity and the Expectations Formation Process: A Simple Framework and New Facts," American Economic Review, American Economic Association, vol. 105(8), pages 2644-2678, August.
    4. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
    5. Christopher D. Carroll, 2003. "Macroeconomic Expectations of Households and Professional Forecasters," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 118(1), pages 269-298.
    6. Angelico, Cristina & Marcucci, Juri & Miccoli, Marcello & Quarta, Filippo, 2022. "Can we measure inflation expectations using Twitter?," Journal of Econometrics, Elsevier, vol. 228(2), pages 259-277.
    7. Christopher A Sims, 2009. "Inflation expectations, uncertainty and monetary policy," BIS Working Papers 275, Bank for International Settlements.
    8. Michael Woodford, 2004. "Inflation targeting and optimal monetary policy," Review, Federal Reserve Bank of St. Louis, vol. 86(Jul), pages 15-42.
    9. Oleksandr Zholud & Volodymyr Lepushynskyi & Sergiy Nikolaychuk, 2019. "The Effectiveness of the Monetary Transmission Mechanism in Ukraine since the Transition to Inflation Targeting," Visnyk of the National Bank of Ukraine, National Bank of Ukraine, issue 247, pages 19-37.
    10. Azqueta-Gavaldón, Andrés, 2017. "Developing news-based Economic Policy Uncertainty index with unsupervised machine learning," Economics Letters, Elsevier, vol. 158(C), pages 47-50.
    11. Mazumder, Sandeep, 2021. "The reaction of inflation forecasts to news about the Fed," Economic Modelling, Elsevier, vol. 94(C), pages 256-264.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chee-Hong Law & Kim Huat Goh, 2024. "A systematic literature review of the implications of media on inflation expectations," International Economics and Economic Policy, Springer, vol. 21(2), pages 311-340, May.
    2. Baranowski, Paweł & Doryń, Wirginia & Łyziak, Tomasz & Stanisławska, Ewa, 2021. "Words and deeds in managing expectations: Empirical evidence from an inflation targeting economy," Economic Modelling, Elsevier, vol. 95(C), pages 49-67.
    3. Czudaj, Robert L., 2022. "Heterogeneity of beliefs and information rigidity in the crude oil market: Evidence from survey data," European Economic Review, Elsevier, vol. 143(C).
    4. Philippe Andrade & Gaetano Gaballo & Eric Mengus & Benoît Mojon, 2019. "Forward Guidance and Heterogeneous Beliefs," American Economic Journal: Macroeconomics, American Economic Association, vol. 11(3), pages 1-29, July.
    5. J. Daniel Aromí & Martín Llada, 2024. "Are professional forecasters inattentive to public discussions? The case of inflation in Argentina," Working Papers 300, Red Nacional de Investigadores en Economía (RedNIE).
    6. Emanuele Ciani & Adeline Delavande & Ben Etheridge & Marco Francesconi, 2023. "Policy Uncertainty and Information Flows: Evidence from Pension Reform Expectations," The Economic Journal, Royal Economic Society, vol. 133(649), pages 98-129.
    7. Tanaka, Mari & Bloom, Nicholas & David, Joel M. & Koga, Maiko, 2020. "Firm performance and macro forecast accuracy," Journal of Monetary Economics, Elsevier, vol. 114(C), pages 26-41.
    8. J. Daniel Aromí & Martín Llada, 2024. "Are professional forecasters inattentive to public discussions about inflation? The case of Argentina," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(7), pages 2572-2587, November.
    9. Perico Ortiz, Daniel, 2023. "Inflation news coverage, expectations and risk premium," FAU Discussion Papers in Economics 05/2023, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    10. Theresa Kuchler & Basit Zafar, 2019. "Personal Experiences and Expectations about Aggregate Outcomes," Journal of Finance, American Finance Association, vol. 74(5), pages 2491-2542, October.
    11. Martin Geiger & Johann Scharler, 2021. "How Do People Interpret Macroeconomic Shocks? Evidence from U.S. Survey Data," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 53(4), pages 813-843, June.
    12. Paul Hubert, 2014. "FOMC Forecasts as a Focal Point for Private Expectations," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 46(7), pages 1381-1420, October.
    13. Fetzer, Thiemo & Yotzov, Ivan, 2023. "(How) Do electoral surprises drive business cycles? Evidence from a new dataset," CAGE Online Working Paper Series 672, Competitive Advantage in the Global Economy (CAGE).
    14. Beckmann, Joscha & Czudaj, Robert L., 2023. "Perceived monetary policy uncertainty," Journal of International Money and Finance, Elsevier, vol. 130(C).
    15. Larsen, Vegard H. & Thorsrud, Leif Anders & Zhulanova, Julia, 2021. "News-driven inflation expectations and information rigidities," Journal of Monetary Economics, Elsevier, vol. 117(C), pages 507-520.
    16. Serafín Frache & Rodrigo Lluberas, 2017. "New information and inflation expectations among firms," Documentos de trabajo 2017013, Banco Central del Uruguay.
    17. Paul Hubert, 2015. "The Influence and Policy Signalling Role of FOMC Forecasts," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(5), pages 655-680, October.
    18. Döpke Jörg & Fritsche Ulrich & Waldhof Gabi, 2019. "Theories, Techniques and the Formation of German Business Cycle Forecasts : Evidence from a survey of professional forecasters," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 239(2), pages 203-241, April.
    19. Cornand, Camille & Hubert, Paul, 2020. "On the external validity of experimental inflation forecasts: A comparison with five categories of field expectations," Journal of Economic Dynamics and Control, Elsevier, vol. 110(C).
    20. Conrad, Christian & Lahiri, Kajal, 2024. "Heterogeneous Expectations among Professional Forecasters," Working Papers 0754, University of Heidelberg, Department of Economics.

    More about this item

    Keywords

    Inflation expectations; natural language processing; textual data; machine learning;
    All these keywords.

    JEL classification:

    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gii:giihei:heidwp13-2021. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Dorina Dobre (email available below). General contact details of provider: https://edirc.repec.org/data/ieheich.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.