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Text Mining-based Economic Activity Estimation

Author

Listed:
  • Ksenia Yakovleva

    (Bank of Russia)

Abstract

This paper outlines a methodology for constructing a high-frequency indicator of economic activity in Russia. News stories from internet resources are used as data sources. News data is analyzed using text mining and machine learning methods, which, although developed relatively recently, have quickly found wide application in scientific research, including economic studies. This is because news is not only a key source of information but a way to gauge the sentiment of journalists and survey respondents about the current situation and convert it into quantitative data.

Suggested Citation

  • Ksenia Yakovleva, 2018. "Text Mining-based Economic Activity Estimation," Russian Journal of Money and Finance, Bank of Russia, vol. 77(4), pages 26-41, December.
    • Handle: RePEc:bkr:journl:v:77:y:2018:i:4:p:26-41
    DOI: 10.31477/rjmf.201804.26
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    References listed on IDEAS

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    1. Dirk Ulbricht & Konstantin A. Kholodilin & Tobias Thomas, 2017. "Do Media Data Help to Predict German Industrial Production?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(5), pages 483-496, August.
    2. 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.
    3. Leif Anders Thorsrud, 2020. "Words are the New Numbers: A Newsy Coincident Index of the Business Cycle," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(2), pages 393-409, April.
    4. Ardia, David & Bluteau, Keven & Boudt, Kris, 2019. "Questioning the news about economic growth: Sparse forecasting using thousands of news-based sentiment values," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1370-1386.
    5. Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, June.
    6. Galbraith, John W. & Tkacz, Greg, 2015. "Nowcasting GDP with electronic payments data," Statistics Paper Series 10, European Central Bank.
    7. I. Goloshchapova & M. Andreev., 2017. "Measuring inflation expectations of the Russian population with the help of machine learning," VOPROSY ECONOMIKI, N.P. Redaktsiya zhurnala "Voprosy Economiki", vol. 6.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Filipp Ulyankin, 2020. "Forecasting Russian Macroeconomic Indicators Based on Information from News and Search Queries," Russian Journal of Money and Finance, Bank of Russia, vol. 79(4), pages 75-97, December.
    2. Petrova, Diana & Trunin, Pavel, 2020. "Revealing the mood of economic agents based on search queries," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 59, pages 71-87.
    3. Mikhaylov, Dmitry, 2023. "Macroeconomic Forecasting with the Use of News Data," Working Papers w20220250, Russian Presidential Academy of National Economy and Public Administration.
    4. Stankevich, Ivan, 2023. "Application of Markov-Switching MIDAS models to nowcasting of GDP and its components," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 70, pages 122-143.
    5. Oleg Semiturkin & Andrey Shevelev, 2023. "Correct Comparison of Predictive Features of Machine Learning Models: The Case of Forecasting Inflation Rates in Siberia," Russian Journal of Money and Finance, Bank of Russia, vol. 82(1), pages 87-103, March.
    6. Stankevich, Ivan, 2020. "Comparison of macroeconomic indicators nowcasting methods: Russian GDP case," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 59, pages 113-127.

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    More about this item

    Keywords

    economic activity estimates; nowcasting; text mining; machine learning; Big Data; data mining; topic modelling; sentiment analysis;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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