IDEAS home Printed from https://ideas.repec.org/p/bcb/wpaper/559.html

Forecasting with VAR-teXt and DFM-teXt Models:exploring the predictive power of central bank communication

Author

Listed:
  • Leonardo N. Ferreira

Abstract

This paper explores the complementarity between traditional econometrics and machine learning and applies the resulting model – the VAR-teXt – to central bank communication. The VAR-teXt is a vector autoregressive (VAR) model augmented with information retrieved from text, turned into quantitative data via a Latent Dirichlet Allocation (LDA) model, whereby the number of topics (or textual factors) is chosen based on their predictive performance. A Markov chain Monte Carlo (MCMC) sampling algorithm for the estimation of the VAR-teXt that takes into account the fact that the textual factors are estimates is also provided. The approach is then extended to dynamic factor models (DFM) generating the DFM-teXt. Results show that textual factors based on Federal Open Market Committee (FOMC) statements are indeed useful for forecasting.

Suggested Citation

  • Leonardo N. Ferreira, 2021. "Forecasting with VAR-teXt and DFM-teXt Models:exploring the predictive power of central bank communication," Working Papers Series 559, Central Bank of Brazil, Research Department.
    • Handle: RePEc:bcb:wpaper:559
    as

    Download full text from publisher

    File URL: https://www.bcb.gov.br/content/publicacoes/WorkingPaperSeries/WP559.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Stephen Hansen & Michael McMahon, 2016. "Shocking Language: Understanding the Macroeconomic Effects of Central Bank Communication," NBER Chapters, in: NBER International Seminar on Macroeconomics 2015, National Bureau of Economic Research, Inc.
    2. Hansen, Stephen & McMahon, Michael & Tong, Matthew, 2019. "The long-run information effect of central bank communication," Journal of Monetary Economics, Elsevier, vol. 108(C), pages 185-202.
    3. Marta Bańbura & Domenico Giannone & Lucrezia Reichlin, 2010. "Large Bayesian vector auto regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 71-92, January.
    4. David Bholat & Stephen Hans & Pedro Santos & Cheryl Schonhardt-Bailey, 2015. "Text mining for central banks," Handbooks, Centre for Central Banking Studies, Bank of England, number 33, April.
    5. Eleni Kalamara & Arthur Turrell & Chris Redl & George Kapetanios & Sujit Kapadia, 2022. "Making text count: Economic forecasting using newspaper text," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 896-919, August.
    6. Piergiorgio Alessandri & Haroon Mumtaz, 2017. "Financial conditions and density forecasts for US output and inflation," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 24, pages 66-78, March.
    7. 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.
    8. Michael W. McCracken & Serena Ng, 2016. "FRED-MD: A Monthly Database for Macroeconomic Research," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 574-589, October.
    9. Simon Gilchrist & Egon Zakrajsek, 2012. "Credit Spreads and Business Cycle Fluctuations," American Economic Review, American Economic Association, vol. 102(4), pages 1692-1720, June.
    10. Stephen Hansen & Michael McMahon & Andrea Prat, 2018. "Transparency and Deliberation Within the FOMC: A Computational Linguistics Approach," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(2), pages 801-870.
    11. Hayo, Bernd & Neuenkirch, Matthias, 2010. "Do Federal Reserve communications help predict federal funds target rate decisions?," Journal of Macroeconomics, Elsevier, vol. 32(4), pages 1014-1024, December.
    12. Marek Jarociński & Peter Karadi, 2020. "Deconstructing Monetary Policy Surprises—The Role of Information Shocks," American Economic Journal: Macroeconomics, American Economic Association, vol. 12(2), pages 1-43, April.
    13. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    14. Geweke, John & Amisano, Gianni, 2010. "Comparing and evaluating Bayesian predictive distributions of asset returns," International Journal of Forecasting, Elsevier, vol. 26(2), pages 216-230, April.
    15. repec:spo:wpmain:info:hdl:2441/52p48pif5099i9i8uilpqhgnt4 is not listed on IDEAS
    16. Forni, Mario & Gambetti, Luca, 2014. "Sufficient information in structural VARs," Journal of Monetary Economics, Elsevier, vol. 66(C), pages 124-136.
    17. Margaret E. Roberts & Brandon M. Stewart & Edoardo M. Airoldi, 2016. "A Model of Text for Experimentation in the Social Sciences," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(515), pages 988-1003, July.
    18. Ben S. Bernanke & Jean Boivin & Piotr Eliasz, 2005. "Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 120(1), pages 387-422.
    19. Raffaella Giacomini & Halbert White, 2006. "Tests of Conditional Predictive Ability," Econometrica, Econometric Society, vol. 74(6), pages 1545-1578, November.
    20. repec:ulb:ulbeco:2013/13388 is not listed on IDEAS
    21. Mark Gertler & Peter Karadi, 2015. "Monetary Policy Surprises, Credit Costs, and Economic Activity," American Economic Journal: Macroeconomics, American Economic Association, vol. 7(1), pages 44-76, January.
    22. Thomas Lustenberger & Enzo Rossi, 2020. "Does Central Bank Transparency and Communication Affect Financial and Macroeconomic Forecasts?," International Journal of Central Banking, International Journal of Central Banking, vol. 16(2), pages 153-201, March.
    23. Jeffrey R. Campbell & Charles L. Evans & Jonas D.M. Fisher & Alejandro Justiniano, 2012. "Macroeconomic Effects of Federal Reserve Forward Guidance," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 43(1 (Spring), pages 1-80.
    24. Larsen, Vegard H. & Thorsrud, Leif A., 2019. "The value of news for economic developments," Journal of Econometrics, Elsevier, vol. 210(1), pages 203-218.
    25. Jing Cynthia Wu & Fan Dora Xia, 2016. "Measuring the Macroeconomic Impact of Monetary Policy at the Zero Lower Bound," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 48(2-3), pages 253-291, March.
    26. Christophe Blot & Paul Hubert, 2018. "Central bank communication during normal and crisis time," Sciences Po Economics Publications (main) hal-03404315, HAL.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Douglas Kiarelly Godoy de Araujo, 2023. "gingado: a machine learning library focused on economics and finance," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Data science in central banking: applications and tools, volume 59, Bank for International Settlements.

    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. Hubert, Paul & Labondance, Fabien, 2021. "The signaling effects of central bank tone," European Economic Review, Elsevier, vol. 133(C).
    2. Ferreira, Leonardo N., 2022. "Forward guidance matters: Disentangling monetary policy shocks," Journal of Macroeconomics, Elsevier, vol. 73(C).
    3. Ahrens, Maximilian & Erdemlioglu, Deniz & McMahon, Michael & Neely, Christopher J. & Yang, Xiye, 2025. "Mind your language: Market responses to central bank speeches," Journal of Econometrics, Elsevier, vol. 249(PC).
    4. Ostapenko, Nataliia, 2020. "Central Bank Communication: Information and Policy shocks," MPRA Paper 101278, University Library of Munich, Germany, revised 21 Jun 2020.
    5. Jon Ellingsen & Vegard H. Larsen & Leif Anders Thorsrud, 2020. "News Media vs. FRED-MD for Macroeconomic Forecasting," CESifo Working Paper Series 8639, CESifo.
    6. Aeimit Lakdawala, 2019. "Decomposing the effects of monetary policy using an external instruments SVAR," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(6), pages 934-950, September.
    7. Lin, Jianhao & Mei, Ziwei & Chen, Liangyuan & Zhu, Chuanqi, 2023. "Is the People's Bank of China consistent in words and deeds?," China Economic Review, Elsevier, vol. 78(C).
    8. Antón Sarabia Arturo & Bazdresch Santiago & Lelo-de-Larrea Alejandra, 2023. "The Influence of Central Bank's Projections and Economic Narrative on Professional Forecasters' Expectations: Evidence from Mexico," Working Papers 2023-21, Banco de México.
    9. Daniel A. Dias & João B. Duarte, 2019. "Monetary policy, housing rents, and inflation dynamics," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(5), pages 673-687, August.
    10. Bennett Schmanski & Chiara Scotti & Clara Vega, 2023. "Fed Communication, News, Twitter, and Echo Chambers," Finance and Economics Discussion Series 2023-036, Board of Governors of the Federal Reserve System (U.S.).
    11. Gardner, Ben & Scotti, Chiara & Vega, Clara, 2022. "Words speak as loudly as actions: Central bank communication and the response of equity prices to macroeconomic announcements," Journal of Econometrics, Elsevier, vol. 231(2), pages 387-409.
    12. Robert Goodhead & Benedikt Kolb, 2025. "Monetary policy communication shocks and the macroeconomy," Economica, London School of Economics and Political Science, vol. 92(365), pages 173-198, January.
    13. repec:spo:wpmain:info:hdl:2441/4evh7bju58uep3gd1frcn5nr9 is not listed on IDEAS
    14. Paul Hubert & Fabien Labondance, 2016. "Central Bank Sentiment and Policy Expectations," Sciences Po Economics Publications (main) hal-03459227, HAL.
    15. Giorgia De Nora, 2021. "Factor Augmented Vector-Autoregression with narrative identification. An application to monetary policy in the US," Working Papers 934, Queen Mary University of London, School of Economics and Finance.
    16. 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.
    17. repec:spo:wpmain:info:hdl:2441/7mota32nad8aopst8f7d5aebpo is not listed on IDEAS
    18. Stephen Hansen & Michael McMahon, 2016. "Shocking Language: Understanding the Macroeconomic Effects of Central Bank Communication," NBER Chapters, in: NBER International Seminar on Macroeconomics 2015, National Bureau of Economic Research, Inc.
    19. Hüpper, Florian & Kempa, Bernd, 2023. "Inflation targeting and inflation communication of the Federal Reserve: Words and deeds," Journal of Macroeconomics, Elsevier, vol. 75(C).
    20. Stefano Fasani & Haroon Mumtaz & Lorenza Rossi, 2023. "Monetary Policy and Firm Dynamics," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 47, pages 278-296, January.
    21. Carlos Alba & Julio A. Carrillo & Raúl Ibarra, 2024. "Information Effects of US Monetary Policy Announcements on Emerging Economies: Evidence from Mexico," Working Papers 2024-14, Banco de México.
    22. repec:spo:wpmain:info:hdl:2441/64veevce0i99oav223j3pkv1hf is not listed on IDEAS
    23. Bu, Chunya & Rogers, John & Wu, Wenbin, 2021. "A unified measure of Fed monetary policy shocks," Journal of Monetary Economics, Elsevier, vol. 118(C), pages 331-349.

    More about this item

    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:bcb:wpaper:559. 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: Rodrigo Barbone Gonzalez (email available below). General contact details of provider: https://www.bcb.gov.br/en .

    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.