Making text count: economic forecasting using newspaper text

Eleni Kalamara, Arthur Turrell, Chris Redl, George Kapetanios and Sujit Kapadia
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Eleni Kalamara: King’s College London

No 865, Bank of England working papers from Bank of England

Abstract: We consider the best way to extract timely signals from newspaper text and use them to forecast macroeconomic variables using three popular UK newspapers that collectively represent UK newspaper readership in terms of political perspective and editorial style. We find that newspaper text can improve economic forecasts both in absolute and marginal terms. We introduce a powerful new method of incorporating text information in forecasts that combines counts of terms with supervised machine learning techniques. This method improves forecasts of macroeconomic variables including GDP, inflation, and unemployment, including relative to existing text-based methods. Forecast improvements occur when it matters most, during stressed periods.

Keywords: Text; forecasting; machine learning (search for similar items in EconPapers)
JEL-codes: C55 J42 (search for similar items in EconPapers)
Pages: 49 pages
Date: 2020-05-22
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ecm and nep-for
References: View complete reference list from CitEc
Citations: View citations in EconPapers (37)

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Journal Article: Making text count: Economic forecasting using newspaper text (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:boe:boeewp:0865

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