IDEAS home Printed from https://ideas.repec.org/p/bcb/wpaper/559.html
   My bibliography  Save this paper

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. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. Christophe Blot & Paul Hubert, 2018. "Central bank communication during normal and crisis time," SciencePo Working papers Main hal-03404315, HAL.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. repec:ulb:ulbeco:2013/13388 is not listed on IDEAS
    17. Marta Banbura & Domenico Giannone & Lucrezia Reichlin, 2010. "Large Bayesian vector auto regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 71-92.
    18. 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.
    19. 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.
    20. 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.
    21. Simon Gilchrist & Egon Zakrajsek, 2012. "Credit Spreads and Business Cycle Fluctuations," American Economic Review, American Economic Association, vol. 102(4), pages 1692-1720, June.
    22. Marek Jarocinski & Peter Karadi, 2017. "Central Bank Information Shocks," 2017 Meeting Papers 1193, Society for Economic Dynamics.
    23. Raffaella Giacomini & Halbert White, 2006. "Tests of Conditional Predictive Ability," Econometrica, Econometric Society, vol. 74(6), pages 1545-1578, November.
    24. Marta Banbura & Domenico Giannone & Lucrezia Reichlin, 2010. "Large Bayesian vector auto regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 71-92.
    25. Forni, Mario & Gambetti, Luca, 2014. "Sufficient information in structural VARs," Journal of Monetary Economics, Elsevier, vol. 66(C), pages 124-136.
    26. Larsen, Vegard H. & Thorsrud, Leif A., 2019. "The value of news for economic developments," Journal of Econometrics, Elsevier, vol. 210(1), pages 203-218.
    27. repec:hal:spmain:info:hdl:2441/52p48pif5099i9i8uilpqhgnt4 is not listed on IDEAS
    28. 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.
    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. Ostapenko, Nataliia, 2020. "Central Bank Communication: Information and Policy shocks," MPRA Paper 101278, University Library of Munich, Germany, revised 21 Jun 2020.
    2. Hubert, Paul & Labondance, Fabien, 2021. "The signaling effects of central bank tone," European Economic Review, Elsevier, vol. 133(C).
    3. Ferreira, Leonardo N., 2022. "Forward guidance matters: Disentangling monetary policy shocks," Journal of Macroeconomics, Elsevier, vol. 73(C).
    4. 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.
    5. Ahrens, Maximilian & Erdemlioglu, Deniz & Mcmahon, Michael & Neely, Christopher J & Yang, Xiye, 2023. "Mind Your Language: Market Responses to Central Bank Speeches," CEPR Discussion Papers 18191, C.E.P.R. Discussion Papers.
    6. 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.
    7. Jon Ellingsen & Vegard H. Larsen & Leif Anders Thorsrud, 2020. "News Media vs. FRED-MD for Macroeconomic Forecasting," CESifo Working Paper Series 8639, CESifo.
    8. 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.
    9. 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.
    10. 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).
    11. 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.
    12. repec:bny:wpaper:0091 is not listed on IDEAS
    13. van der Zwan, Terri & Kole, Erik & van der Wel, Michel, 2024. "Heterogeneous macro and financial effects of ECB asset purchase programs," Journal of International Money and Finance, Elsevier, vol. 143(C).
    14. Paul Hubert & Fabien Labondance, 2016. "Central Bank Sentiment and Policy Expectations," SciencePo Working papers Main hal-03459227, HAL.
    15. Christopher S. Sutherland, 2020. "Forward Guidance and Expectation Formation: A Narrative Approach," Staff Working Papers 20-40, Bank of Canada.
    16. Donato Masciandaro & Davide Romelli & Gaia Rubera, 2021. "Monetary policy and financial markets: evidence from Twitter traffic," BAFFI CAREFIN Working Papers 21160, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    17. Mirela Miescu & Haroon Mumtaz, 2019. "Proxy structural vector autoregressions, informational sufficiency and the role of monetary policy," Working Papers 280730188, Lancaster University Management School, Economics Department.
    18. Berg, Tim O. & Henzel, Steffen R., 2015. "Point and density forecasts for the euro area using Bayesian VARs," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1067-1095.
    19. 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.
    20. Giancarlo Corsetti & Joao B Duarte & Samuel Mann, 2022. "One Money, Many Markets [Fixed Rate Versus Adjustable Rate Mortgages: Evidence from Euro Area Banks]," Journal of the European Economic Association, European Economic Association, vol. 20(1), pages 513-548.
    21. 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.

    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.