Linking words in economic discourse: Implications for macroeconomic forecasts

J. Daniel Aromi

International Journal of Forecasting, 2020, vol. 36, issue 4, 1517-1530

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.

Keywords: Macroeconomic forecasting; Text-based data; Combining forecasts; Natural language processing; Uncertainty (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:36:y:2020:i:4:p:1517-1530

DOI: 10.1016/j.ijforecast.2019.12.001

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