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
- Institución
- Pontificia Universidad Católica Argentina
- OAI Identificador
- oai:ucacris:123456789/10786
Ver los metadatos del registro completo
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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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1836638353458462720 |
score |
13.070432 |