IDEAS home Printed from https://ideas.repec.org/p/bis/bisiwp/14.html
   My bibliography  Save this paper

Big data: the hunt for timely insights and decision certainty

Author

Listed:
  • Per Nymand-Andersen

Abstract

A new data paradigm has emerged. Despite the human instinct to reject what cannot be fully comprehended, the big data industry is extracting new causations among multiple pools of micro-data that previously looked unrelated. This is leading to new, timely indicators and insights, and may generate new economic theories. Central banks do not have to be ahead of the curve, but they should not miss this opportunity to extract economic signals in almost real time, learn from the new methodologies, enhance their economic forecasts and obtain more precise and timely evaluations of the impact of their policies. Moreover, they should encourage these new data sources to be transparent regarding their methodology, quality and aggregation methods for publishing new types of economic indicators. Lastly, the big data industry will challenge not only traditional statistics and economics, but also the way in which these are fed into the decision-making process. This paper argues in favour of developing a conceptual framework and road map for central banks using relevant pilot studies. The objective is to explore the conditions for making systematic use of these sources as part of the central banking policy toolkit.

Suggested Citation

  • Per Nymand-Andersen, 2016. "Big data: the hunt for timely insights and decision certainty," IFC Working Papers 14, Bank for International Settlements.
  • Handle: RePEc:bis:bisiwp:14
    as

    Download full text from publisher

    File URL: https://www.bis.org/ifc/publ/ifcwork14.pdf
    File Function: Full PDF document
    Download Restriction: no

    File URL: https://www.bis.org/ifc/publ/ifcwork14.htm
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. D’Amuri, Francesco & Marcucci, Juri, 2017. "The predictive power of Google searches in forecasting US unemployment," International Journal of Forecasting, Elsevier, vol. 33(4), pages 801-816.
    2. David M Bholat, 2013. "The future of central bank data," Journal of Banking Regulation, Palgrave Macmillan, vol. 14(3-4), pages 185-194, July.
    3. Meltem Gulenay Chadwick & Gonul Sengul, 2015. "Nowcasting the Unemployment Rate in Turkey : Let's ask Google," Central Bank Review, Research and Monetary Policy Department, Central Bank of the Republic of Turkey, vol. 15(3), pages 15-40.
    4. Brose,Margarita S. & Flood,Mark D. & Krishna,Dilip & Nichols,Bill (ed.), 2014. "Handbook of Financial Data and Risk Information I," Cambridge Books, Cambridge University Press, number 9781107012011, September.
    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. Götz, Thomas B. & Knetsch, Thomas A., 2019. "Google data in bridge equation models for German GDP," International Journal of Forecasting, Elsevier, vol. 35(1), pages 45-66.
    2. Vegard H. Larsen & Leif Anders Thorsrud, 2018. "Business cycle narratives," Working Paper 2018/3, Norges Bank.
    3. repec:bny:wpaper:0046 is not listed on IDEAS
    4. repec:bny:wpaper:0064 is not listed on IDEAS

    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. Tuhkuri, Joonas, 2016. "Forecasting Unemployment with Google Searches," ETLA Working Papers 35, The Research Institute of the Finnish Economy.
    2. Mihaela, Simionescu, 2020. "Improving unemployment rate forecasts at regional level in Romania using Google Trends," Technological Forecasting and Social Change, Elsevier, vol. 155(C).
    3. Simionescu, Mihaela & Cifuentes-Faura, Javier, 2022. "Can unemployment forecasts based on Google Trends help government design better policies? An investigation based on Spain and Portugal," Journal of Policy Modeling, Elsevier, vol. 44(1), pages 1-21.
    4. Nakamura, Nobuyuki & Suzuki, Aya, 2021. "COVID-19 and the intentions to migrate from developing countries: Evidence from online search activities in Southeast Asia," Journal of Asian Economics, Elsevier, vol. 76(C).
    5. Mihaela Simionescu & Javier Cifuentes-Faura, 2022. "Forecasting National and Regional Youth Unemployment in Spain Using Google Trends," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 164(3), pages 1187-1216, December.
    6. Simionescu, Mihaela & Raišienė, Agota Giedrė, 2021. "A bridge between sentiment indicators: What does Google Trends tell us about COVID-19 pandemic and employment expectations in the EU new member states?," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    7. Tuhkuri, Joonas, 2016. "ETLAnow: A Model for Forecasting with Big Data – Forecasting Unemployment with Google Searches in Europe," ETLA Reports 54, The Research Institute of the Finnish Economy.
    8. Antoaneta Serguieva, 2017. "Multichannel Contagion vs Stabilisation in Multiple Interconnected Financial Markets," Papers 1701.06975, arXiv.org, revised Apr 2017.
    9. Bentzen, Jeanet Sinding, 2021. "In crisis, we pray: Religiosity and the COVID-19 pandemic," Journal of Economic Behavior & Organization, Elsevier, vol. 192(C), pages 541-583.
    10. Riccardo De Bonis & Matteo Piazza, 2021. "A silent revolution. How central bank statistics have changed in the last 25 years," PSL Quarterly Review, Economia civile, vol. 74(299), pages 347-371.
    11. Jacques Bughin, 2015. "Google searches and twitter mood: nowcasting telecom sales performance," Netnomics, Springer, vol. 16(1), pages 87-105, August.
    12. Matteo Iacopini & Carlo R.M.A. Santagiustina, 2021. "Filtering the intensity of public concern from social media count data with jumps," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(4), pages 1283-1302, October.
    13. Antoaneta Serguieva & David Bholat, 2017. "Multichannel contagion vs stabilisation in multiple interconnected financial markets," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Statistical implications of the new financial landscape, volume 43, Bank for International Settlements.
    14. Ferrara, Laurent & Sheng, Xuguang Simon, 2022. "Guest editorial: Economic forecasting in times of COVID-19," International Journal of Forecasting, Elsevier, vol. 38(2), pages 527-528.
    15. Monge, Manuel & Claudio-Quiroga, Gloria & Poza, Carlos, 2024. "Chinese economic behavior in times of covid-19. A new leading economic indicator based on Google trends," International Economics, Elsevier, vol. 177(C).
    16. Laurent Ferrara & Anna Simoni, 2023. "When are Google Data Useful to Nowcast GDP? An Approach via Preselection and Shrinkage," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(4), pages 1188-1202, October.
    17. Cebrián, Eduardo & Domenech, Josep, 2024. "Addressing Google Trends inconsistencies," Technological Forecasting and Social Change, Elsevier, vol. 202(C).
    18. Neto, David, 2021. "Are Google searches making the Bitcoin market run amok? A tail event analysis," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    19. Perroni, Carlo & Scharf, Kimberley & Talavera, Oleksandr & Vi, Linh, 2022. "Does online salience predict charitable giving? Evidence from SMS text donations," Journal of Economic Behavior & Organization, Elsevier, vol. 197(C), pages 134-149.
    20. Necmettin Alpay Koçak, 2020. "The Role of Ecb Speeches in Nowcasting German Gdp," European Financial and Accounting Journal, Prague University of Economics and Business, vol. 2020(2), pages 05-20.

    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:bis:bisiwp:14. 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: Martin Fessler (email available below). General contact details of provider: https://edirc.repec.org/data/bisssch.html .

    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.