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Construction and Analysis of Uncertainty Indices based on Multilingual Text Representations

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  • Viktoriia Naboka-Krell

    (University Giessen)

Abstract

The work by Baker et al. (2016), who propose a dictionary based method and estimate the level of economic policy uncertainty (EPU) based on the occurrence of specific terms in ten leading newspapers in the USA, is among the first ones to detect the potential of text data in economic research. Following this line of research, this paper proposes automated approaches to construction of EPU indices for different countries based on newspapers’ texts. First, multilingual fastText word embeddings and BERT text embeddings are used in order to define relevant EPU key words and EPU related articles, respectively. Further, multilingual conceptualized topic modeling introduced by Bianchi et al. (2021) is performed and EPU related topics are detected. It is shown that the constructed EPU indices based on fastText embeddings Granger cause the economic activity in all of the considered countries, namely Germany, Russia, and Ukraine. Also, some of the topics uncovered by multilingual conceptualized topic modeling have proved to Granger cause the economic activity in all of the considered countries.

Suggested Citation

  • Viktoriia Naboka-Krell, 2023. "Construction and Analysis of Uncertainty Indices based on Multilingual Text Representations," MAGKS Papers on Economics 202310, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    • Handle: RePEc:mar:magkse:202310
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    References listed on IDEAS

    as
    1. David Lenz & Peter Winker, 2020. "Measuring the diffusion of innovations with paragraph vector topic models," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-18, January.
    2. Ivana Lolić & Petar Sorić & Marija Logarušić, 2022. "Economic Policy Uncertainty Index Meets Ensemble Learning," Computational Economics, Springer;Society for Computational Economics, vol. 60(2), pages 401-437, August.
    3. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
    4. Ghirelli, Corinna & Pérez, Javier J. & Urtasun, Alberto, 2019. "A new economic policy uncertainty index for Spain," Economics Letters, Elsevier, vol. 182(C), pages 64-67.
    5. Felix Kapfhammer & Vegard H. Larsen & Leif Anders Thorsrud, 2020. "Climate risk and commodity currencies," Working Paper 2020/18, Norges Bank.
    6. Xie, Fangzhou, 2020. "Wasserstein Index Generation Model: Automatic generation of time-series index with application to Economic Policy Uncertainty," Economics Letters, Elsevier, vol. 186(C).
    7. Manela, Asaf & Moreira, Alan, 2017. "News implied volatility and disaster concerns," Journal of Financial Economics, Elsevier, vol. 123(1), pages 137-162.
    8. Azqueta-Gavaldón, Andrés, 2017. "Developing news-based Economic Policy Uncertainty index with unsupervised machine learning," Economics Letters, Elsevier, vol. 158(C), pages 47-50.
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    Cited by:

    1. Moritz Grebe & Sinem Kandemir & Peter Tillmann, 2023. "Uncertainty about the War in Ukraine: Measurement and Effects on the German Business Cycle," MAGKS Papers on Economics 202314, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    2. Grebe, Moritz & Kandemir, Sinem & Tillmann, Peter, 2023. "Uncertainty about the war in Ukraine: Measurement and effects on the German business cycle," IMFS Working Paper Series 184, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS).

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    Keywords

    text-as-data; fastText emeddings; BERT; economic policy uncertainty; natural language processing;
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