Forecasting inflation with Twitter

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Abstract: Inflation has become a central topic in macroeconomic analysis. In this context, there is high value in expanding our ability to monitor and, more importantly, anticipate inflation dynamics. Social media content emerges as a potentially valuable tool to advance this agenda. That is, the...

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Detalles Bibliográficos
Autores principales: Aromí, José Daniel, Llada, Martín
Otros Autores: 0000-0002-4377-532X
Formato: Documento de trabajo
Lenguaje:Inglés
Publicado: 2024
Materias:
MACROECONOMÍA
INFLACION
REDES SOCIALES
DEVALUACION
TWITTER
Acceso en línea:https://repositorio.uca.edu.ar/handle/123456789/18354
Aporte de:
Repositorio Institucional de la Universidad Católica Argentina (UCA) de Universidad Católica Argentina
id I33-R139-123456789-18354
record_format dspace
spelling I33-R139-123456789-183542024-06-28T05:02:21Z Forecasting inflation with Twitter Aromí, José Daniel Llada, Martín 0000-0002-4377-532X MACROECONOMÍA INFLACION REDES SOCIALES DEVALUACION TWITTER Abstract: Inflation has become a central topic in macroeconomic analysis. In this context, there is high value in expanding our ability to monitor and, more importantly, anticipate inflation dynamics. Social media content emerges as a potentially valuable tool to advance this agenda. That is, the large volume of messages exchanged in public discussions can be used to extract information regarding the likely path of inflation dynamics. In this study, we analyze public discussions on the micro-blogging site Twitter. Our study focuses on the case of Argentina. This is a particularly interesting case of study since this is an economy where inflation has been a recurrent and highly disruptive phenomenon. The empirical evidence indicates Twitter content anticipates inflation. More specifically, a simple indicator of the level of attention allocated to inflation provides valuable information regarding inflation levels and inflation uncertainty. Estimated forecasting models indicate that an increment in the attention index are followed to statistically and economically significant increments in expected inflation. Out-of-sample forecasts confirm that the index allows for gains in forecast accuracy. Complementarily, higher values of the attention index anticipate increments in inflation uncertainty as approximated by the interquartile range of next month inflation forecasts. The information gains are different from and compare favorably with the information provided by lagged inflation and lagged devaluation rate. Also, analyses show that these information gains are substantive compared to those that result from using traditional macroeconomic indicators such as the level of economic activity, monetary aggregates and interest rates. Furthermore, the information content of four alternative indicators of expectations are also evaluated: Google search Volume, newspaper content, mass media tweets and a consumer survey. These analyses confirm that social media data constitutes a particularly valuable source of information regarding future inflation. 2024-06-27T16:25:35Z 2024-06-27T16:25:35Z 2022 Documento de trabajo Forecasting inflation with Twitter [en línea]. Serie Documentos de Trabajo del IIEP. 2022, 76. Disponible en: https://repositorio.uca.edu.ar/handle/123456789/18354 2451-5728 https://repositorio.uca.edu.ar/handle/123456789/18354 eng Acceso abierto http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Argentina SIGLO XX Serie Documentos de Trabajo del IIEP. 2022, 76
institution Universidad Católica Argentina
institution_str I-33
repository_str R-139
collection Repositorio Institucional de la Universidad Católica Argentina (UCA)
language Inglés
topic MACROECONOMÍA
INFLACION
REDES SOCIALES
DEVALUACION
TWITTER
spellingShingle MACROECONOMÍA
INFLACION
REDES SOCIALES
DEVALUACION
TWITTER
Aromí, José Daniel
Llada, Martín
Forecasting inflation with Twitter
topic_facet MACROECONOMÍA
INFLACION
REDES SOCIALES
DEVALUACION
TWITTER
description Abstract: Inflation has become a central topic in macroeconomic analysis. In this context, there is high value in expanding our ability to monitor and, more importantly, anticipate inflation dynamics. Social media content emerges as a potentially valuable tool to advance this agenda. That is, the large volume of messages exchanged in public discussions can be used to extract information regarding the likely path of inflation dynamics. In this study, we analyze public discussions on the micro-blogging site Twitter. Our study focuses on the case of Argentina. This is a particularly interesting case of study since this is an economy where inflation has been a recurrent and highly disruptive phenomenon. The empirical evidence indicates Twitter content anticipates inflation. More specifically, a simple indicator of the level of attention allocated to inflation provides valuable information regarding inflation levels and inflation uncertainty. Estimated forecasting models indicate that an increment in the attention index are followed to statistically and economically significant increments in expected inflation. Out-of-sample forecasts confirm that the index allows for gains in forecast accuracy. Complementarily, higher values of the attention index anticipate increments in inflation uncertainty as approximated by the interquartile range of next month inflation forecasts. The information gains are different from and compare favorably with the information provided by lagged inflation and lagged devaluation rate. Also, analyses show that these information gains are substantive compared to those that result from using traditional macroeconomic indicators such as the level of economic activity, monetary aggregates and interest rates. Furthermore, the information content of four alternative indicators of expectations are also evaluated: Google search Volume, newspaper content, mass media tweets and a consumer survey. These analyses confirm that social media data constitutes a particularly valuable source of information regarding future inflation.
author2 0000-0002-4377-532X
author_facet 0000-0002-4377-532X
Aromí, José Daniel
Llada, Martín
format Documento de trabajo
author Aromí, José Daniel
Llada, Martín
author_sort Aromí, José Daniel
title Forecasting inflation with Twitter
title_short Forecasting inflation with Twitter
title_full Forecasting inflation with Twitter
title_fullStr Forecasting inflation with Twitter
title_full_unstemmed Forecasting inflation with Twitter
title_sort forecasting inflation with twitter
publishDate 2024
url https://repositorio.uca.edu.ar/handle/123456789/18354
work_keys_str_mv AT aromijosedaniel forecastinginflationwithtwitter
AT lladamartin forecastinginflationwithtwitter
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