IDEAS home Printed from https://ideas.repec.org/p/bdr/borrec/1256.html
   My bibliography  Save this paper

Tweeting Inflation: Real-Time measures of Inflation Perception in Colombia

Author

Listed:
  • Jonathan Alexander Muñoz-Martínez
  • David Orozco
  • Mario A. Ramos-Veloza

Abstract

This study follows a novel approach proposed by Angelico et al. (2022) using Twitter to measure inflation perception in Colombia in real time. By applying machine learning techniques, we implement two real-time indicators of inflation perception and show that both exhibit a dynamic similar to inflation and inflation expectations for the sample period January 2015 to March 2023. Our interpretation of these results suggests that our indicators are closely linked to the underlying factors that drive inflation perception. Overall, this approach provides a valuable instrument for gauging public sentiment towards inflation and complements the traditional inflation expectations measures used in the inflation–targeting framework. **** RESUMEN: Este estudio sigue un enfoque novedoso propuesto por Angelico et al. (2022) para la medición en tiempo real de la percepción de la inflación en Colombia utilizando Twitter. Mediante la aplicación de técnicas de aprendizaje automático, calculamos dos indicadores en tiempo real de la percepción de la inflación y mostramos que exhiben una dinámica comparable a la inflación y las expectativas de inflación, lo que sugiere que nuestros indicadores están estrechamente relacionados con los factores subyacentes que impulsan la percepción de la inflación entre enero de 2015 y marzo de 2023. En general, este enfoque proporciona un medio valioso para evaluar el sentimiento público hacia la inflación y ofrece una perspectiva complementaria a las medidas de expectativas de inflación tradicionales utilizadas en el marco de la política de inflación objetivo.

Suggested Citation

  • Jonathan Alexander Muñoz-Martínez & David Orozco & Mario A. Ramos-Veloza, 2023. "Tweeting Inflation: Real-Time measures of Inflation Perception in Colombia," Borradores de Economia 1256, Banco de la Republica de Colombia.
  • Handle: RePEc:bdr:borrec:1256
    DOI: 10.32468/be.1256
    as

    Download full text from publisher

    File URL: https://doi.org/10.32468/be.1256
    Download Restriction: no

    File URL: https://libkey.io/10.32468/be.1256?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Dolan Antenucci & Michael Cafarella & Margaret Levenstein & Christopher Ré & Matthew D. Shapiro, 2014. "Using Social Media to Measure Labor Market Flows," NBER Working Papers 20010, National Bureau of Economic Research, Inc.
    2. 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.
    3. Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2006. "A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 499-526.
    4. Jeannine Bailliu & Xinfen Han & Mark Kruger & Yu-Hsien Liu & Sri Thanabalasingam, 2019. "Can media and text analytics provide insights into labour market conditions in China?," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Are post-crisis statistical initiatives completed?, volume 49, Bank for International Settlements.
    5. Grant, Alan P. & Thomas, Lloyd B., 1999. "Inflationary expectations and rationality revisited," Economics Letters, Elsevier, vol. 62(3), pages 331-338, March.
    6. 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.
    7. Serkan Cicek & Cuneyt Akar, 2014. "Do Inflation Expectations Converge Toward Inflation Target or Actual Inflation? Evidence from Expectation Gap Persistence," Central Bank Review, Research and Monetary Policy Department, Central Bank of the Republic of Turkey, vol. 14(1), pages 15-21.
    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. Ang, Andrew & Bekaert, Geert & Wei, Min, 2007. "Do macro variables, asset markets, or surveys forecast inflation better?," Journal of Monetary Economics, Elsevier, vol. 54(4), pages 1163-1212, May.
    2. Eric Ghysels & Leonardo Iania & Jonas Striaukas, 2018. "Quantile-based Inflation Risk Models," Working Paper Research 349, National Bank of Belgium.
    3. Carlos Medel, 2017. "Forecasting Chilean inflation with the hybrid new keynesian Phillips curve: globalisation, combination, and accuracy," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 20(3), pages 004-050, December.
    4. Edith Skriner, 2008. "Forecasting Global Flows," FIW Working Paper series 009, FIW.
    5. Galvão, Ana Beatriz, 2013. "Changes in predictive ability with mixed frequency data," International Journal of Forecasting, Elsevier, vol. 29(3), pages 395-410.
    6. Eliana González & Luis F. Melo & Viviana Monroy & Brayan Rojas, 2009. "A Dynamic Factor Model For The Colombian Inflation," Borradores de Economia 5273, Banco de la Republica.
    7. Giulio Cornelli & Sebastian Doerr & Leonardo Gambacorta & Bruno Tissot, 2022. "Big Data in Asian Central Banks," Asian Economic Policy Review, Japan Center for Economic Research, vol. 17(2), pages 255-269, July.
    8. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2015. "Realtime nowcasting with a Bayesian mixed frequency model with stochastic volatility," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(4), pages 837-862, October.
    9. Baris Soybilgen & Ege Yazgan, 2017. "An evaluation of inflation expectations in Turkey," Central Bank Review, Research and Monetary Policy Department, Central Bank of the Republic of Turkey, vol. 17(1), pages 1-31–38.
    10. Koop, Gary & Korobilis, Dimitris, 2013. "Large time-varying parameter VARs," Journal of Econometrics, Elsevier, vol. 177(2), pages 185-198.
    11. repec:hal:spmain:info:hdl:2441/74362fq3f99s299n07e84dlcib is not listed on IDEAS
    12. Pesaran, M.H. & Pick, A. & Timmermann, A., 2009. "Variable Selection and Inference for Multi-period Forecasting Problems," Cambridge Working Papers in Economics 0901, Faculty of Economics, University of Cambridge.
    13. Michael W. McCracken & Michael T. Owyang & Tatevik Sekhposyan, 2021. "Real-Time Forecasting and Scenario Analysis Using a Large Mixed-Frequency Bayesian VAR," International Journal of Central Banking, International Journal of Central Banking, vol. 17(71), pages 1-41, December.
    14. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2008. "Forecasting Macroeconomic Variables Using Diffusion Indexes in Short Samples with Structural Change," Economics Working Papers ECO2008/17, European University Institute.
    15. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "How is machine learning useful for macroeconomic forecasting?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
    16. Karapanagiotidis, Paul, 2013. "Empirical evidence for nonlinearity and irreversibility of commodity futures prices," MPRA Paper 56801, University Library of Munich, Germany.
    17. Ferrari, Davide & Ravazzolo, Francesco & Vespignani, Joaquin, 2021. "Forecasting energy commodity prices: A large global dataset sparse approach," Energy Economics, Elsevier, vol. 98(C).
    18. Zhao, Yuanying & Pawlak, Jacek & Sivakumar, Aruna, 2022. "Theory for socio-demographic enrichment performance using the inverse discrete choice modelling approach," Transportation Research Part B: Methodological, Elsevier, vol. 155(C), pages 101-134.
    19. Kock, Anders Bredahl & Teräsvirta, Timo, 2014. "Forecasting performances of three automated modelling techniques during the economic crisis 2007–2009," International Journal of Forecasting, Elsevier, vol. 30(3), pages 616-631.
    20. Dake Li & Mikkel Plagborg-M{o}ller & Christian K. Wolf, 2021. "Local Projections vs. VARs: Lessons From Thousands of DGPs," Papers 2104.00655, arXiv.org, revised Jan 2024.
    21. John M. Abowd & Ian M. Schmutte & William Sexton & Lars Vilhuber, 2019. "Suboptimal Provision of Privacy and Statistical Accuracy When They are Public Goods," Papers 1906.09353, arXiv.org.

    More about this item

    Keywords

    Inflation perceptions; Twitter; Real-time data; Central banks; Percepción de inflación; Twitter; medición en tiempo real; Bancos centrales.;
    All these keywords.

    JEL classification:

    • 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

    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:1256. 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: Clorith Angélica Bahos Olivera (email available below). General contact details of provider: https://edirc.repec.org/data/brcgvco.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.