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News-driven inflation expectations and information rigidities

Author

Listed:
  • Larsen, Vegard H.
  • Thorsrud, Leif Anders
  • Zhulanova, Julia

Abstract

Using a large news corpus and machine learning algorithms we investigate the role played by the media in the expectations formation process of households, and conclude that the news topics media report on are good predictors of both inflation and inflation expectations. In turn, in a noisy information model, augmented with a simple media channel, we document that the time series features of relevant topics help explain time-varying information rigidity among households. As such, we provide a novel estimate of state-dependent information rigidities and present new evidence highlighting the role of the media in understanding inflation expectations and information rigidities.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:moneco:v:117:y:2021:i:c:p:507-520
    DOI: 10.1016/j.jmoneco.2020.03.004
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    7. Saskia Ter Ellen & Vegard H. Larsen & Leif Anders Thorsrud, 2022. "Narrative Monetary Policy Surprises and the Media," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 54(5), pages 1525-1549, August.
    8. Daniel J. Lewis & Christos Makridis & Karel Mertens, 2019. "Do Monetary Policy Announcements Shift Household Expectations?," Working Papers 1906, Federal Reserve Bank of Dallas, revised 17 Jan 2020.
    9. Müller, Henrik & Schmidt, Tobias & Rieger, Jonas & Hufnagel, Lena Marie & Hornig, Nico, 2022. "A German inflation narrative. How the media frame price dynamics: Results from a RollingLDA analysis," DoCMA Working Papers 9, TU Dortmund University, Dortmund Center for Data-based Media Analysis (DoCMA).
    10. Felix Kapfhammer & Vegard H. Larsen & Leif Anders Thorsrud, 2020. "Climate Risk and Commodity Currencies," Working Papers No 10/2020, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    11. Mazumder, Sandeep, 2021. "The reaction of inflation forecasts to news about the Fed," Economic Modelling, Elsevier, vol. 94(C), pages 256-264.
    12. Andres Algaba & David Ardia & Keven Bluteau & Samuel Borms & Kris Boudt, 2020. "Econometrics Meets Sentiment: An Overview Of Methodology And Applications," Journal of Economic Surveys, Wiley Blackwell, vol. 34(3), pages 512-547, July.
    13. Kim Ristolainen & Tomi Roukka & Henri Nyberg, 2021. "A Thousand Words Tell More Than Just Numbers: Financial Crises and Historical Headlines," Discussion Papers 149, Aboa Centre for Economics.
    14. Dietrich, Alexander M. & Kuester, Keith & Müller, Gernot J. & Schoenle, Raphael, 2022. "News and uncertainty about COVID-19: Survey evidence and short-run economic impact," Journal of Monetary Economics, Elsevier, vol. 129(S), pages 35-51.
    15. Alistair Macaulay, 2022. "Heterogeneous Information, Subjective Model Beliefs, and the Time-Varying Transmission of Shocks," CESifo Working Paper Series 9733, CESifo.
    16. Ardia, David & Bluteau, Keven & Boudt, Kris, 2022. "Media abnormal tone, earnings announcements, and the stock market," Journal of Financial Markets, Elsevier, vol. 61(C).
    17. Bertsch, Christoph & Hull, Isaiah & Zhang, Xin, 2021. "Narrative fragmentation and the business cycle," Economics Letters, Elsevier, vol. 201(C).
    18. Wenting Song & Samuel Stern, 2022. "Firm Inattention and the Efficacy of Monetary Policy: A Text-Based Approach," Staff Working Papers 22-3, Bank of Canada.
    19. Yuting Chen & Don Bredin & Valerio Potì & Roman Matkovskyy, 2022. "COVID risk narratives: a computational linguistic approach to the econometric identification of narrative risk during a pandemic," Digital Finance, Springer, vol. 4(1), pages 17-61, March.
    20. Lange, Kai-Robin & Reccius, Matthias & Schmidt, Tobias & Müller, Henrik & Roos, Michael W. M. & Jentsch, Carsten, 2022. "Towards extracting collective economic narratives from texts," Ruhr Economic Papers 963, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    21. Sonan Memon, 2021. "Machine Learning for Economists: An Introduction," The Pakistan Development Review, Pakistan Institute of Development Economics, vol. 60(2), pages 201-211.
    22. Dorine Boumans & Henrik Müller & Stefan Sauer, 2022. "How Media Content Influences Economic Expectations: Evidence from a Global Expert Survey," ifo Working Paper Series 380, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    23. Ashwin,Julian & Rao,Vijayendra & Biradavolu,Monica Rao & Chhabra,Aditya & Haque,Arshia & Khan,Afsana Iffat & Krishnan,Nandini, 2022. "A Method to Scale-Up Interpretative Qualitative Analysis, with an Application toAspirations in Cox’s Bazaar, Bangladesh," Policy Research Working Paper Series 10046, The World Bank.
    24. Bai, Xiwen & Lam, Jasmine Siu Lee & Jakher, Astha, 2021. "Shipping sentiment and the dry bulk shipping freight market: New evidence from newspaper coverage," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 155(C).
    25. Petrova, Diana, 2022. "Assessment of inflation expectations based on internet data," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 66, pages 25-38.

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    More about this item

    Keywords

    Expectations; Media; Machine learning; Inflation;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • E13 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Neoclassical
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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