Making text count: Economic forecasting using newspaper text

Summary This paper examines several ways to extract timely economic signals from newspaper text and shows that such information can materially improve forecasts of macroeconomic variables including GDP, inflation and unemployment. Our text is drawn from three popular UK newspapers that collectively...

Full description

Saved in:
Bibliographic Details
Published in:Journal of applied econometrics (Chichester, England) England), 2022-08, Vol.37 (5), p.896-919
Main Authors: Kalamara, Eleni, Turrell, Arthur, Redl, Chris, Kapetanios, George, Kapadia, Sujit
Format: Article
Language:English
Subjects:
Conditioning
Econometrics
Economic forecasting
Economic forecasts
Economic stress
Economics
forecasting
Inflation
Machine learning
Readership
text
Unemployment
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Summary This paper examines several ways to extract timely economic signals from newspaper text and shows that such information can materially improve forecasts of macroeconomic variables including GDP, inflation and unemployment. Our text is drawn from three popular UK newspapers that collectively represent UK newspaper readership in terms of political perspective and editorial style. Exploiting newspaper text can improve economic forecasts both unconditionally and when conditioning on other relevant information, but the performance of the latter varies according to the method used. Incorporating text into forecasts by combining counts of terms with supervised machine learning delivers the highest forecast improvements relative to existing text‐based methods. These improvements are most pronounced during periods of economic stress when, arguably, forecasts matter most.
ISSN:0883-7252
1099-1255
DOI:10.1002/jae.2907