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Do daily lead texts help nowcasting GDP growth?

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  • Marc Burri

Abstract

This paper evaluates whether publicly available daily news lead texts help nowcasting Swiss GDP growth. I collect titles and lead texts from three Swiss newspapers and calculate text-based indicators for various economic concepts. A composite indicator calculated from these indicators is highly correlated with low-frequency macroeconomic data and survey-based indicators. In a pseudo out-of-sample nowcasting exercise for Swiss GDP growth, the indicator outperforms a monthly Swiss business cycle indicator if one month of information is available. Improvements in nowcasting accuracy mainly occur in times of economic distress.

Suggested Citation

  • Marc Burri, 2023. "Do daily lead texts help nowcasting GDP growth?," IRENE Working Papers 23-02, IRENE Institute of Economic Research.
  • Handle: RePEc:irn:wpaper:23-02
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    File URL: https://www5.unine.ch/RePEc/ftp/irn/pdfs/WP23-02.pdf
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    References listed on IDEAS

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    1. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    2. Shapiro, Adam Hale & Sudhof, Moritz & Wilson, Daniel J., 2022. "Measuring news sentiment," Journal of Econometrics, Elsevier, vol. 228(2), pages 221-243.
    3. Klaus Abberger & Michael Graff & Boriss Siliverstovs & Jan-Egbert Sturm, 2014. "The KOF Economic Barometer, Version 2014," KOF Working papers 14-353, KOF Swiss Economic Institute, ETH Zurich.
    4. Vegard Høghaug Larsen, 2021. "Components Of Uncertainty," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 62(2), pages 769-788, May.
    5. Wegmüller, Philipp & Glocker, Christian & Guggia, Valentino, 2023. "Weekly economic activity: Measurement and informational content," International Journal of Forecasting, Elsevier, vol. 39(1), pages 228-243.
    6. H. P. Luhn, 1960. "Key word‐in‐context index for technical literature (kwic index)," American Documentation, Wiley Blackwell, vol. 11(4), pages 288-295, October.
    7. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-162, April.
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    More about this item

    Keywords

    Mixed-frequency data; composite leading indicator; news sentiment; recession; natural language processing; nowcasting;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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