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Título : | Linking words in economic discourse: implications for macroeconomic forecasts | Autor : | Aromí, José Daniel | Palabras clave : | MACROECONOMIA; ANALISIS DE DATOS; INDICADORES ECONOMICOS; PREVISIONES ECONOMICAS | Fecha de publicación : | 2020 | Editorial : | Elsevier | Cita : | 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 | Proyecto: | Estudios de estados subjetivos en contextos microeconómicos | Resumen : | 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. | URI : | https://repositorio.uca.edu.ar/handle/123456789/10786 | ISSN : | 0169-2070 | Disciplina: | ECONOMIA | DOI: | https://doi.org/10.1016/j.ijforecast.2019.12.001 0169-2070 | Derechos: | Acceso abierto. 24 meses de embargo |
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