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Big data: the hunt for timely insights and decision certainty

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  • 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
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    References listed on IDEAS

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    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, October.
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    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. Leif Anders Thorsrud, 2016. "Nowcasting using news topics. Big Data versus big bank," Working Paper 2016/20, Norges Bank.
    4. Leif Anders Thorsrud, 2016. "Nowcasting using news topics. Big Data versus big bank," Working Paper 2016/20, Norges Bank.
    5. Vegard H. Larsen & Leif Anders Thorsrud, 2018. "Business cycle narratives," Working Paper 2018/3, Norges Bank.

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