Linking words in economic discourse: implications for macroeconomic forecasts

Autores
Aromí, José Daniel
Año de publicación
2020
Idioma
inglés
Tipo de recurso
artículo
Estado
versión aceptada
Descripción
Fil: Aromí, José Daniel. Universidad de Buenos Aires. Instituto Interdisciplinario de Economía Política de Buenos Aires; Argentina
Fil: Aromí, José Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas de Argentina; Argentina
Fil: Aromí, José Daniel. Pontificia Universidad Católica Argentina. Facultad de Ciencias Económicas; Argentina
Abstract: This paper develops indicators of unstructured press information by exploiting word vector representations. A model is trained using a corpus covering 90 years of Wall Street Journal content. The information content of the indicators is assessed through business cycle forecast exercises. The vector representations can learn meaningful word associations that are exploited to construct indicators of uncertainty. In-sample and out-of-sample forecast exercises show that the indicators contain valuable information regarding future economic activity. The combination of indices associated with different subjective states (e.g., uncertainty, fear, pessimism) results in further gains in information content. The documented performance is unmatched by previous dictionary-based word counting techniques proposed in the literature.
Fuente
International Journal of Forecasting Vol.36, No.4, 2020
Materia
MACROECONOMIA
ANALISIS DE DATOS
INDICADORES ECONOMICOS
PREVISIONES ECONOMICAS
Nivel de accesibilidad
acceso embargado
Condiciones de uso
Repositorio
Repositorio Institucional (UCA)
Institución
Pontificia Universidad Católica Argentina
OAI Identificador
oai:ucacris:123456789/10786
Acceder
id RIUCA_7060dbc2cab45f5267f753e929a712bd
oai_identifier_str oai:ucacris:123456789/10786
network_acronym_str RIUCA
repository_id_str 2585
network_name_str Repositorio Institucional (UCA)
spelling Linking words in economic discourse: implications for macroeconomic forecastsAromí, José DanielMACROECONOMIAANALISIS DE DATOSINDICADORES ECONOMICOSPREVISIONES ECONOMICASFil: Aromí, José Daniel. Universidad de Buenos Aires. Instituto Interdisciplinario de Economía Política de Buenos Aires; ArgentinaFil: Aromí, José Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas de Argentina; ArgentinaFil: Aromí, José Daniel. Pontificia Universidad Católica Argentina. Facultad de Ciencias Económicas; ArgentinaAbstract: This paper develops indicators of unstructured press information by exploiting word vector representations. A model is trained using a corpus covering 90 years of Wall Street Journal content. The information content of the indicators is assessed through business cycle forecast exercises. The vector representations can learn meaningful word associations that are exploited to construct indicators of uncertainty. In-sample and out-of-sample forecast exercises show that the indicators contain valuable information regarding future economic activity. The combination of indices associated with different subjective states (e.g., uncertainty, fear, pessimism) results in further gains in information content. The documented performance is unmatched by previous dictionary-based word counting techniques proposed in the literature.Elsevierinfo:eu-repo/date/embargoEnd/2022-10-012020info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttps://repositorio.uca.edu.ar/handle/123456789/107860169-2070https://doi.org/10.1016/j.ijforecast.2019.12.001 0169-2070Aromí, J. D. Linking words in economic discourse: implications for macroeconomic forecasts [en línea]. International Journal of Forecasting. 2020, 36 (4). Disponible en: https://repositorio.uca.edu.ar/handle/123456789/10786International Journal of Forecasting Vol.36, No.4, 2020reponame:Repositorio Institucional (UCA)instname:Pontificia Universidad Católica ArgentinaengEstudios de estados subjetivos en contextos microeconómicosinfo:eu-repo/semantics/embargoedAccess2025-07-03T10:57:33Zoai:ucacris:123456789/10786instacron:UCAInstitucionalhttps://repositorio.uca.edu.ar/Universidad privadaNo correspondehttps://repositorio.uca.edu.ar/oaiclaudia_fernandez@uca.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:25852025-07-03 10:57:33.98Repositorio Institucional (UCA) - Pontificia Universidad Católica Argentinafalse
dc.title.none.fl_str_mv Linking words in economic discourse: implications for macroeconomic forecasts
title Linking words in economic discourse: implications for macroeconomic forecasts
spellingShingle Linking words in economic discourse: implications for macroeconomic forecasts
Aromí, José Daniel
MACROECONOMIA
ANALISIS DE DATOS
INDICADORES ECONOMICOS
PREVISIONES ECONOMICAS
title_short Linking words in economic discourse: implications for macroeconomic forecasts
title_full Linking words in economic discourse: implications for macroeconomic forecasts
title_fullStr Linking words in economic discourse: implications for macroeconomic forecasts
title_full_unstemmed Linking words in economic discourse: implications for macroeconomic forecasts
title_sort Linking words in economic discourse: implications for macroeconomic forecasts
dc.creator.none.fl_str_mv Aromí, José Daniel
author Aromí, José Daniel
author_facet Aromí, José Daniel
author_role author
dc.subject.none.fl_str_mv MACROECONOMIA
ANALISIS DE DATOS
INDICADORES ECONOMICOS
PREVISIONES ECONOMICAS
topic MACROECONOMIA
ANALISIS DE DATOS
INDICADORES ECONOMICOS
PREVISIONES ECONOMICAS
dc.description.none.fl_txt_mv Fil: Aromí, José Daniel. Universidad de Buenos Aires. Instituto Interdisciplinario de Economía Política de Buenos Aires; Argentina
Fil: Aromí, José Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas de Argentina; Argentina
Fil: Aromí, José Daniel. Pontificia Universidad Católica Argentina. Facultad de Ciencias Económicas; Argentina
Abstract: This paper develops indicators of unstructured press information by exploiting word vector representations. A model is trained using a corpus covering 90 years of Wall Street Journal content. The information content of the indicators is assessed through business cycle forecast exercises. The vector representations can learn meaningful word associations that are exploited to construct indicators of uncertainty. In-sample and out-of-sample forecast exercises show that the indicators contain valuable information regarding future economic activity. The combination of indices associated with different subjective states (e.g., uncertainty, fear, pessimism) results in further gains in information content. The documented performance is unmatched by previous dictionary-based word counting techniques proposed in the literature.
description Fil: Aromí, José Daniel. Universidad de Buenos Aires. Instituto Interdisciplinario de Economía Política de Buenos Aires; Argentina
publishDate 2020
dc.date.none.fl_str_mv 2020
info:eu-repo/date/embargoEnd/2022-10-01
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://repositorio.uca.edu.ar/handle/123456789/10786
0169-2070
https://doi.org/10.1016/j.ijforecast.2019.12.001 0169-2070
Aromí, J. D. Linking words in economic discourse: implications for macroeconomic forecasts [en línea]. International Journal of Forecasting. 2020, 36 (4). Disponible en: https://repositorio.uca.edu.ar/handle/123456789/10786
url https://repositorio.uca.edu.ar/handle/123456789/10786
https://doi.org/10.1016/j.ijforecast.2019.12.001 0169-2070
identifier_str_mv 0169-2070
Aromí, J. D. Linking words in economic discourse: implications for macroeconomic forecasts [en línea]. International Journal of Forecasting. 2020, 36 (4). Disponible en: https://repositorio.uca.edu.ar/handle/123456789/10786
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Estudios de estados subjetivos en contextos microeconómicos
dc.rights.none.fl_str_mv info:eu-repo/semantics/embargoedAccess
eu_rights_str_mv embargoedAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv International Journal of Forecasting Vol.36, No.4, 2020
reponame:Repositorio Institucional (UCA)
instname:Pontificia Universidad Católica Argentina
reponame_str Repositorio Institucional (UCA)
collection Repositorio Institucional (UCA)
instname_str Pontificia Universidad Católica Argentina
repository.name.fl_str_mv Repositorio Institucional (UCA) - Pontificia Universidad Católica Argentina
repository.mail.fl_str_mv claudia_fernandez@uca.edu.ar
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score 12.982451

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