IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0291862.html

Which news topics drive economic prosperity in China?

Author

Listed:
  • Wanbo Lu
  • Yifu Wang
  • Xingjian Zhang

Abstract

Precise and real-time measurements of economic prosperity are vital to a country’s economic system. This study aims to identify news topics that promoted economic prosperity in China from 2011–2021. By extracting economic topics from news text data, we construct a news coincidence index with comprehensive information and strong timeliness and reveal the trend of topic contribution. The Latent Dirichlet Allocation (LDA) topic model is applied to extract economic topics from the news. We use a mixed-frequency dynamic factor model to track rapid economic development without using high-frequency weekly and daily data. We identify the six most influential topics and investigate their evolution, which may serve as a reference for economic construction and regulation.

Suggested Citation

  • Wanbo Lu & Yifu Wang & Xingjian Zhang, 2023. "Which news topics drive economic prosperity in China?," PLOS ONE, Public Library of Science, vol. 18(10), pages 1-23, October.
    • Handle: RePEc:plo:pone00:0291862
    DOI: 10.1371/journal.pone.0291862
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0291862
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0291862&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0291862?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Huang, Yun & Luk, Paul, 2020. "Measuring economic policy uncertainty in China," China Economic Review, Elsevier, vol. 59(C).
    2. Paul Beaudry & Franck Portier, 2014. "News-Driven Business Cycles: Insights and Challenges," Journal of Economic Literature, American Economic Association, vol. 52(4), pages 993-1074, December.
    3. Diamantis Petropoulos Petalas & Hein van Schie & Paul Hendriks Vettehen, 2017. "Forecasted economic change and the self-fulfilling prophecy in economic decision-making," PLOS ONE, Public Library of Science, vol. 12(3), pages 1-18, March.
    4. Clément Bortoli & Stéphanie Combes & Thomas Renault, 2018. "Nowcasting GDP Growth by Reading the Newspapers," Economie et Statistique / Economics and Statistics, Institut National de la Statistique et des Etudes Economiques (INSEE), issue 505-506, pages 17-33.
    5. Bańbura, Marta & Rünstler, Gerhard, 2011. "A look into the factor model black box: Publication lags and the role of hard and soft data in forecasting GDP," International Journal of Forecasting, Elsevier, vol. 27(2), pages 333-346.
    6. Vegard Høghaug Larsen & Leif Anders Thorsrud, 2022. "Asset returns, news topics, and media effects," Scandinavian Journal of Economics, Wiley Blackwell, vol. 124(3), pages 838-868, July.
    7. Larsen, Vegard H. & Thorsrud, Leif A., 2019. "The value of news for economic developments," Journal of Econometrics, Elsevier, vol. 210(1), pages 203-218.
    8. Jiang, Fuwei & Lee, Joshua & Martin, Xiumin & Zhou, Guofu, 2019. "Manager sentiment and stock returns," Journal of Financial Economics, Elsevier, vol. 132(1), pages 126-149.
    9. Aguilar, Pablo & Ghirelli, Corinna & Pacce, Matías & Urtasun, Alberto, 2021. "Can news help measure economic sentiment? An application in COVID-19 times," Economics Letters, Elsevier, vol. 199(C).
    10. Jon Ellingsen & Vegard H. Larsen & Leif Anders Thorsrud, 2022. "News media versus FRED‐MD for macroeconomic forecasting," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(1), pages 63-81, January.
    11. 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.
    12. Aruoba, S. BoraÄŸan & Diebold, Francis X. & Scotti, Chiara, 2009. "Real-Time Measurement of Business Conditions," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 417-427.
    13. Shapiro, Adam Hale & Sudhof, Moritz & Wilson, Daniel J., 2022. "Measuring news sentiment," Journal of Econometrics, Elsevier, vol. 228(2), pages 221-243.
    14. James H. Stock & Mark W. Watson, 1989. "New Indexes of Coincident and Leading Economic Indicators," NBER Chapters, in: NBER Macroeconomics Annual 1989, Volume 4, pages 351-409, National Bureau of Economic Research, Inc.
    15. Rambaccussing, Dooruj & Kwiatkowski, Andrzej, 2020. "Forecasting with news sentiment: Evidence with UK newspapers," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1501-1516.
    16. Xu, Zhaoxia, 2020. "Economic policy uncertainty, cost of capital, and corporate innovation," Journal of Banking & Finance, Elsevier, vol. 111(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhenghui Li & Zhongxiu Chen & Zhehao Huang, 2026. "Modelling the data-generating mechanism of China’s commodity market by identifying hidden information flow regimes," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 12(1), pages 1-28, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Vegard H. Larsen & Leif Anders Thorsrud, 2018. "Business cycle narratives," Working Paper 2018/3, Norges Bank.
    2. Julian Ashwin & Eleni Kalamara & Lorena Saiz, 2024. "Nowcasting Euro area GDP with news sentiment: A tale of two crises," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(5), pages 887-905, August.
    3. Erik Andres-Escayola & Corinna Ghirelli & Luis Molina & Javier J. Pérez & Elena Vidal, 2022. "Using newspapers for textual indicators: which and how many?," Working Papers 2235, Banco de España.
    4. Luca Barbaglia & Sergio Consoli & Sebastiano Manzan, 2024. "Forecasting GDP in Europe with textual data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(2), pages 338-355, March.
    5. Adämmer, Philipp & Prüser, Jan & Schüssler, Rainer A., 2025. "Forecasting macroeconomic tail risk in real time: Do textual data add value?," International Journal of Forecasting, Elsevier, vol. 41(1), pages 307-320.
    6. Dorinth van Dijk & Jasper de Winter, 2023. "Nowcasting GDP using tone-adjusted time varying news topics: Evidence from the financial press," Working Papers 766, DNB.
    7. Vegard H. Larsen & Leif Anders Thorsrud, 2026. "Using Transformers and Reinforcement Learning as Narrative Filters in Macroeconomics," Working Papers No 02/2026, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    8. Jon Ellingsen & Vegard H. Larsen & Leif Anders Thorsrud, 2022. "News media versus FRED‐MD for macroeconomic forecasting," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(1), pages 63-81, January.
    9. Larsen, Vegard H. & Thorsrud, Leif Anders & Zhulanova, Julia, 2021. "News-driven inflation expectations and information rigidities," Journal of Monetary Economics, Elsevier, vol. 117(C), pages 507-520.
    10. Saiz, Lorena & Magro, Manuel Medina, 2025. "What can newspaper articles reveal about the euro area economy?," Working Paper Series 3122, European Central Bank.
    11. Erik Andres-Escayola & Corinna Ghirelli & Luis Molina & Javier J. Perez & Elena Vidal, 2024. "Using Newspapers for Textual Indicators: Guidance Based on Spanish- and Portuguese-Speaking Countries," Computational Economics, Springer;Society for Computational Economics, vol. 64(2), pages 643-692, August.
    12. Jon Ellingsen & Vegard H. Larsen & Leif Anders Thorsrud, 2020. "News Media vs. FRED-MD for Macroeconomic Forecasting," CESifo Working Paper Series 8639, CESifo.
    13. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
    14. Gardner, Ben & Scotti, Chiara & Vega, Clara, 2022. "Words speak as loudly as actions: Central bank communication and the response of equity prices to macroeconomic announcements," Journal of Econometrics, Elsevier, vol. 231(2), pages 387-409.
    15. Elena Andreou & Eric Ghysels & Andros Kourtellos, 2013. "Should Macroeconomic Forecasters Use Daily Financial Data and How?," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(2), pages 240-251, April.
    16. Antolín-Díaz, Juan & Drechsel, Thomas & Petrella, Ivan, 2024. "Advances in nowcasting economic activity: The role of heterogeneous dynamics and fat tails," Journal of Econometrics, Elsevier, vol. 238(2).
    17. Chauvet, Marcelle & Potter, Simon, 2013. "Forecasting Output," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 141-194, Elsevier.
    18. Daniel Borup & Jorge Wolfgang Hansen & Benjamin Dybro Liengaard & Erik Christian Montes Schütte, 2023. "Quantifying investor narratives and their role during COVID‐19," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(4), pages 512-532, June.
    19. Bondarenko, Yevheniia & Lewis, Vivien & Rottner, Matthias & Schüler, Yves, 2024. "Geopolitical risk perceptions," Journal of International Economics, Elsevier, vol. 152(C).
    20. Olivier de Bandt & Jean-Charles Bricongne & Julien Denes & Alexandre Dhenin & Annabelle De Gaye & Pierre-Antoine Robert, 2023. "Using the Press to Construct a New Indicator of Inflation Perceptions in France," Working papers 921, Banque de France.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0291862. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.