IDEAS home Printed from https://ideas.repec.org/p/bdr/borrec/1108.html
   My bibliography  Save this paper

Descripción de las Minutas e Informes de Política Monetaria a partir de herramientas de Lingüística Computacional

Author

Listed:
  • Daniela V. Guío-Martínez

    ()

  • Juan J. Ospina-Tejeiro

    () (Banco de la República de Colombia)

  • Germán A. Muñoz-Bravo

    () (Banco de la República de Colombia)

  • Julián A. Parra-Polanía

    () (Banco de la República de Colombia)

Abstract

Con base en el uso de Latent Dirichlet Allocation, una herramienta de lingüística computacional cuya finalidad es develar los patrones temáticos subyacentes que agrupan las palabras de un conjunto de textos, analizamos dos tipos esenciales de documentos en la comunicación del Banco de la República, las minutas y los informes de política monetaria, para el periodo comprendido entre marzo de 2007 y diciembre de 2018. Encontramos que estos dos tipos de documentos giran primordialmente en torno a ocho temas, siendo el más importante (en promedio a través del tiempo) el que contiene términos principalmente relacionados con demanda interna y sectores económicos. Describimos tanto las similitudes como las diferencias que se observan, entre las minutas y los informes, en la participación de cada tema dentro de los documentos y en la evolución de esa participación en el tiempo. **** ABSTRACT: Based on the use of Latent Dirichlet Allocation, a computational linguistics tool whose purpose is to identify the underlying thematic patterns that group the words of a set of documents, we analyse two essential outlets in the Banco de la Republica’s communication, the minutes and monetary policy reports, from March 2007 to December 2018. We find that these two outlets discuss primarily about eight topics, the most important (on average, over time) being the one that contains expressions mainly related to domestic demand and economic sectors. We describe both similarities and differences that are observed, between the minutes and the reports, in the participation of each topic within the documents and in the evolution of that participation over time.

Suggested Citation

  • Daniela V. Guío-Martínez & Juan J. Ospina-Tejeiro & Germán A. Muñoz-Bravo & Julián A. Parra-Polanía, 2020. "Descripción de las Minutas e Informes de Política Monetaria a partir de herramientas de Lingüística Computacional," Borradores de Economia 1108, Banco de la Republica de Colombia.
  • Handle: RePEc:bdr:borrec:1108
    DOI: https://doi.org/10.32468/be.1108
    as

    Download full text from publisher

    File URL: https://doi.org/10.32468/be.1108
    Download Restriction: no

    References listed on IDEAS

    as
    1. 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, December.
    2. Stephen Hansen & Michael McMahon & Andrea Prat, 2018. "Transparency and Deliberation Within the FOMC: A Computational Linguistics Approach," The Quarterly Journal of Economics, Oxford University Press, vol. 133(2), pages 801-870.
    3. Rodrigo Taborda, 2015. "Procedural transparency in Latin American central banks under inflation targeting schemes. A text analysis of the minutes of the Boards of Directors," Revista ESPE - Ensayos sobre Política Económica, Banco de la Republica de Colombia, vol. 33(76), pages 76-92, April.
    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. Leif Anders Thorsrud, 2016. "Nowcasting using news topics Big Data versus big bank," Working Papers No 6/2016, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    2. Miguel Acosta, 2015. "FOMC Responses to Calls for Transparency," Finance and Economics Discussion Series 2015-60, Board of Governors of the Federal Reserve System (U.S.).
    3. Kawamura, Kohei & Kobashi, Yohei & Shizume, Masato & Ueda, Kozo, 2019. "Strategic central bank communication: Discourse analysis of the Bank of Japan’s Monthly Report," Journal of Economic Dynamics and Control, Elsevier, vol. 100(C), pages 230-250.
    4. Saskia ter Ellen & Vegard H. Larsen & Leif Anders Thorsrud, 2019. "Narrative monetary policy surprises and the media," Working Papers No 06/2019, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    5. Vegard Høghaug Larsen & Leif Anders Thorsrud, 2018. "Business cycle narratives," Working Papers No 6/2018, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    6. Pongsak Luangaram & Warapong Wongwachara, 2017. "More Than Words: A Textual Analysis of Monetary Policy Communication," PIER Discussion Papers 54, Puey Ungphakorn Institute for Economic Research, revised Feb 2017.
    7. Bholat, David & Brookes, James & Cai, Chris & Grundy, Katy & Lund, Jakob, 2017. "Sending firm messages: text mining letters from PRA supervisors to banks and building societies they regulate," Bank of England working papers 688, Bank of England.
    8. Jochen Lüdering & Peter Tillmann, 2016. "Monetary Policy on Twitter and its Effect on Asset Prices: Evidence from Computational Text Analysis," MAGKS Papers on Economics 201612, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    9. Jon Ellingsen & Vegard H. Larsen & Leif Anders Thorsrud, 2020. "News Media vs. FRED-MD for Macroeconomic Forecasting," CESifo Working Paper Series 8639, CESifo.
    10. Mueller, Hannes & Rauh, Christopher, 2018. "Reading Between the Lines: Prediction of Political Violence Using Newspaper Text," American Political Science Review, Cambridge University Press, vol. 112(2), pages 358-375, May.
    11. Aaryan Gupta & Vinya Dengre & Hamza Abubakar Kheruwala & Manan Shah, 2020. "Comprehensive review of text-mining applications in finance," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-25, December.
    12. Paul Hubert & Fabien Labondance, 2016. "Central Bank Sentiment and Policy Expectations," Sciences Po publications 2016-29, Sciences Po.
    13. Amadxarif, Zahid & Brookes, James & Garbarino, Nicola & Patel, Rajan & Walczak, Eryk, 2019. "The language of rules: textual complexity in banking reforms," Bank of England working papers 834, Bank of England.
    14. Luis Fernando Melo-Velandia & Juan J. Ospina-Tejeiro & Julian A. Parra-Polania, 2020. "Effects of Banco de la Republica’s Communication on the Yield Curve," Borradores de Economia 1137, Banco de la Republica de Colombia.
    15. Cour-Thimann, Philippine & Jung, Alexander, 2020. "Interest rate setting and communication at the ECB," Working Paper Series 2443, European Central Bank.
    16. Grajzl, Peter & Murrell, Peter, 2021. "A machine-learning history of English caselaw and legal ideas prior to the Industrial Revolution I: generating and interpreting the estimates," Journal of Institutional Economics, Cambridge University Press, vol. 17(1), pages 1-19, February.
    17. 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, December.
    18. Draca, Mirko & Schwarz, Carlo, 2019. "How Polarized are Citizens? Measuring Ideology from the Ground-Up," The Warwick Economics Research Paper Series (TWERPS) 1218, University of Warwick, Department of Economics.
    19. 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.
    20. David Lenz & Peter Winker, 2020. "Measuring the diffusion of innovations with paragraph vector topic models," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-18, January.

    More about this item

    Keywords

    Comunicación; Política Monetaria; Minería de Texto; LDA; Communication; Monetary Policy; Text Mining; LDA.;

    JEL classification:

    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies
    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General

    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:bdr:borrec:1108. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Clorith Angélica Bahos Olivera). General contact details of provider: http://edirc.repec.org/data/brcgvco.html .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.