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Medición de Incertidumbre Económica en Redes Sociales en Base a Modelos de Procesamiento de Lenguaje Natural

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  • J. Daniel Aromí

    (IIEP UBA-Conicet/UCA)

Abstract

Este trabajo propone un índice que describe las opiniones económicas transmitidas por usuarios argentinos en la red social Twitter. Luego de identificar mensajes económicos, éstos son clasificadas según la frecuencia con la que se utilizan palabras asociadas a incertidumbre. La evaluación cualitativa del índice sugiere un fuerte vínculo con eventos económicos y políticos de relevancia. Estimaciones de modelos estadísticos indican que el índice contiene información sobre el ciclo económico, la confianza del consumidor y la evolución del mercado cambiario. Análisis complementarios demuestran que el foco en el concepto de incertidumbre y el uso de técnicas de procesamiento de lenguaje natural constituyen elementos clave para el desempeño satisfactorio de este indicador de opiniones.

Suggested Citation

  • J. Daniel Aromí, 2022. "Medición de Incertidumbre Económica en Redes Sociales en Base a Modelos de Procesamiento de Lenguaje Natural," Working Papers 179, Red Nacional de Investigadores en Economía (RedNIE).
    • Handle: RePEc:aoz:wpaper:179
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    File URL: https://rednie.eco.unc.edu.ar/files/DT/179.pdf
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    References listed on IDEAS

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    1. Shapiro, Adam Hale & Sudhof, Moritz & Wilson, Daniel J., 2022. "Measuring news sentiment," Journal of Econometrics, Elsevier, vol. 228(2), pages 221-243.
    2. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    3. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
    4. Leland Bybee & Bryan T. Kelly & Asaf Manela & Dacheng Xiu, 2020. "The Structure of Economic News," NBER Working Papers 26648, National Bureau of Economic Research, Inc.
    5. Whitney K. Newey & Kenneth D. West, 1994. "Automatic Lag Selection in Covariance Matrix Estimation," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 61(4), pages 631-653.
    6. José Daniel Aromí, 2017. "Measuring uncertainty through word vector representations," Económica, Departamento de Economía, Facultad de Ciencias Económicas, Universidad Nacional de La Plata, vol. 63, pages 135-156, January-D.
    7. Juan Sebastián Becerra & Andrés Sagner, 2020. "Twitter-Based Economic Policy Uncertainty Index for Chile," Working Papers Central Bank of Chile 883, Central Bank of Chile.
    8. Anna Orlik & Laura Veldkamp, 2014. "Understanding Uncertainty Shocks and the Role of Black Swans," NBER Working Papers 20445, National Bureau of Economic Research, Inc.
    9. Nicholas Bloom, 2009. "The Impact of Uncertainty Shocks," Econometrica, Econometric Society, vol. 77(3), pages 623-685, May.
    10. Aromi, J. Daniel, 2020. "Linking words in economic discourse: Implications for macroeconomic forecasts," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1517-1530.
    11. Paul C. Tetlock, 2007. "Giving Content to Investor Sentiment: The Role of Media in the Stock Market," Journal of Finance, American Finance Association, vol. 62(3), pages 1139-1168, June.
    12. Stiglitz, Joseph E & Weiss, Andrew, 1981. "Credit Rationing in Markets with Imperfect Information," American Economic Review, American Economic Association, vol. 71(3), pages 393-410, June.
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