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Nowcasting with Daily Data

Author

Listed:
  • Michele Modugno

    (ECB)

  • Lucrezia Reichlin

    (London Business School)

  • Domenico Giannone

    (Université Libre de Bruxelles)

  • Marta Banbura

    (European Central Bank)

Abstract

Economists have imperfect knowledge of the present state of the economy and even of the recent past. Many key statistics are released with a long delay and they are subsequently revised. As a consequence, unlike weather forecasters, who know what is the weather today and only have to predict the weather tomorrow, economists have to forecast the present and even the recent past. The problem of predicting the present, the very near future and the very recent past is labelled as nowcasting and is the subject of this paper.

Suggested Citation

  • Michele Modugno & Lucrezia Reichlin & Domenico Giannone & Marta Banbura, 2012. "Nowcasting with Daily Data," 2012 Meeting Papers 555, Society for Economic Dynamics.
    • Handle: RePEc:red:sed012:555
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    References listed on IDEAS

    as
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    Cited by:

    1. Wieland, Volker & Wolters, Maik, 2013. "Forecasting and Policy Making," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 239-325, Elsevier.
    2. Bragoli, Daniela & Modugno, Michele, 2017. "A now-casting model for Canada: Do U.S. variables matter?," International Journal of Forecasting, Elsevier, vol. 33(4), pages 786-800.
    3. Bragoli, Daniela, 2017. "Now-casting the Japanese economy," International Journal of Forecasting, Elsevier, vol. 33(2), pages 390-402.
    4. 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.
    5. Hecq, A.W. & Götz, T.B. & Urbain, J.R.Y.J., 2012. "Real-time forecast density combinations (forecasting US GDP growth using mixed-frequency data)," Research Memorandum 021, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    6. Park, Sungjun & Kim, Jinsoo, 2018. "The effect of interest in renewable energy on US household electricity consumption: An analysis using Google Trends data," Renewable Energy, Elsevier, vol. 127(C), pages 1004-1010.
    7. Davor Kunovac & Borna Špalat, 2014. "Nowcasting GDP Using Available Monthly Indicators," Working Papers 39, The Croatian National Bank, Croatia.

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