IDEAS home Printed from https://ideas.repec.org/p/nsb/bilten/12.html
   My bibliography  Save this paper

Assessment of inflationary pressures using newspaper text analysis

Author

Listed:
  • Mirko Ðukic

    (National Bank of Serbia)

Abstract

In this paper we present the results of a dictionary-based text analysis of articles from the economic sections of four Serbian daily newspapers, caried out to estimate if those articles contain useful information for assessing inflationary pressures. We analyzed 117,113 economic articles in total for the period 2007–2022, by counting terms related to inflation and price rises and price falls, or counting texts containing those terms. Measures of newspaper inflation sentiment, obtained in this way, were found to be highly correlated with inflation, mainly driven by the periods of large inflation swings. During the period of stable inflation, the correlation is significantly lower. Causal relationship clearly goes from the newspaper inflation sentiment to inflation, and simple inflation models with the sentiment as an explanatory variable beat benchmark AR model in the out-ofsample forecast. We conclude that newspaper inflation sentiment can be used as an indicator of inflationary pressures, especially during periods of high inflation volatility.

Suggested Citation

  • Mirko Ðukic, 2022. "Assessment of inflationary pressures using newspaper text analysis," Working Papers Bulletin 12, National Bank of Serbia.
    • Handle: RePEc:nsb:bilten:12
    as

    Download full text from publisher

    File URL: https://www.nbs.rs/export/sites/NBS_site/documents-eng/publikacije/wp_bulletin/wp_bulletin_09_22_2.pdf
    File Function: Full text
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Dräger, Lena & Lamla, Michael J., 2012. "Updating inflation expectations: Evidence from micro-data," Economics Letters, Elsevier, vol. 117(3), pages 807-810.
    3. 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.
    4. David O. Lucca & Francesco Trebbi, 2009. "Measuring Central Bank Communication: An Automated Approach with Application to FOMC Statements," NBER Working Papers 15367, National Bureau of Economic Research, Inc.
    5. Rambaccussing, Dooruj & Kwiatkowski, Andrzej, 2020. "Forecasting with news sentiment: Evidence with UK newspapers," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1501-1516.
    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. de Mendonça, Helder Ferreira & Vereda, Luciano & Araujo, Mateus de Azevedo, 2022. "What type of information calls the attention of forecasters? Evidence from survey data in an emerging market," Journal of International Money and Finance, Elsevier, vol. 129(C).
    2. repec:zbw:bofrdp:2021_010 is not listed on IDEAS
    3. 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.
    4. Viral V. Acharya & Matteo Crosignani & Tim Eisert & Christian Eufinger, 2023. "How Do Supply Shocks to Inflation Generalize? Evidence from the Pandemic Era in Europe," NBER Working Papers 31790, National Bureau of Economic Research, Inc.
    5. 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.
    6. 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.
    7. David A. Comerford, 2024. "Response Bias in Survey Measures of Expectations: Evidence from the Survey of Consumer Expectations’ Inflation Module," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 56(4), pages 933-953, June.
    8. Ryan Chahrour & Kristoffer Nimark & Stefan Pitschner, 2021. "Sectoral Media Focus and Aggregate Fluctuations," American Economic Review, American Economic Association, vol. 111(12), pages 3872-3922, December.
    9. 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.
    10. Jon Ellingsen & Vegard H. Larsen & Leif Anders Thorsrud, 2020. "News Media vs. FRED-MD for Macroeconomic Forecasting," CESifo Working Paper Series 8639, CESifo.
    11. 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.
    12. Gómez-Cram, Roberto & Grotteria, Marco, 2022. "Real-time price discovery via verbal communication: Method and application to Fedspeak," Journal of Financial Economics, Elsevier, vol. 143(3), pages 993-1025.
    13. Stanisławska, Ewa & Paloviita, Maritta, 2021. "Medium- vs. short-term consumer inflation expectations: Evidence from a new euro area survey," Bank of Finland Research Discussion Papers 10/2021, Bank of Finland.
    14. Pfajfar, Damjan & Žakelj, Blaž, 2014. "Experimental evidence on inflation expectation formation," Journal of Economic Dynamics and Control, Elsevier, vol. 44(C), pages 147-168.
    15. Yingying Xu & Zhixin Liu & Zichao Jia & Chi-Wei Su, 2017. "Is time-variant information stickiness state-dependent?," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 16(3), pages 169-187, December.
    16. Mazumder, Sandeep, 2021. "The reaction of inflation forecasts to news about the Fed," Economic Modelling, Elsevier, vol. 94(C), pages 256-264.
    17. Jianhao Lin & Jiacheng Fan & Yifan Zhang & Liangyuan Chen, 2023. "Real‐time macroeconomic projection using narrative central bank communication," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(2), pages 202-221, March.
    18. 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.
    19. Alistair Macaulay, 2022. "Heterogeneous Information, Subjective Model Beliefs, and the Time-Varying Transmission of Shocks," CESifo Working Paper Series 9733, CESifo.
    20. repec:fce:doctra:13-03 is not listed on IDEAS
    21. Jean-Charles Bricongne & Baptiste Meunier & Raquel Caldeira, 2024. "Should Central Banks Care About Text Mining? A Literature Review," Working papers 950, Banque de France.
    22. 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.

    More about this item

    Keywords

    inflation forecasting; text analysis;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • 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
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

    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:nsb:bilten:12. 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: Marko Miseljic (email available below). General contact details of provider: https://edirc.repec.org/data/nbjgvyu.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.