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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
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    References listed on IDEAS

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    1. Raffaella Giacomini & Halbert White, 2006. "Tests of Conditional Predictive Ability," Econometrica, Econometric Society, vol. 74(6), pages 1545-1578, November.
    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. Camilla Salvatore & Silvia Biffignandi & Annamaria Bianchi, 2021. "Social Media and Twitter Data Quality for New Social Indicators," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 156(2), pages 601-630, August.
    4. 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.
    5. 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.
    6. Xu, Yingying & Chang, Hsu-Ling & Lobonţ, Oana-Ramona & Su, Chi-Wei, 2016. "Modeling heterogeneous inflation expectations: empirical evidence from demographic data?," Economic Modelling, Elsevier, vol. 57(C), pages 153-163.
    7. Grant, Alan P. & Thomas, Lloyd B., 1999. "Inflationary expectations and rationality revisited," Economics Letters, Elsevier, vol. 62(3), pages 331-338, March.
    8. 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.
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    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

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