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Nowcasting UK GDP growth

Author

Listed:
  • Bell, Venetia

    (Bank of England)

  • Co, Lai Wah

    (Bank of England)

  • Stone, Sophie

    (Bank of England)

  • Wallis, gavin`

    (Bank of England)

Abstract

Official estimates of UK GDP growth are published with a lag, but other data and statistical models provide an early indication of GDP growth. This article describes the approaches taken by Bank staff to produce early estimates (‘nowcasts’) of GDP growth, ahead of the publication of official estimates. Although the confidence bands around the Bank staff’s nowcasts can be large, these estimates have tended to be more accurate than those from a simple statistical model.

Suggested Citation

  • Bell, Venetia & Co, Lai Wah & Stone, Sophie & Wallis, gavin`, 2014. "Nowcasting UK GDP growth," Bank of England Quarterly Bulletin, Bank of England, vol. 54(1), pages 58-68.
    • Handle: RePEc:boe:qbullt:0132
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    References listed on IDEAS

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    1. Eric Ghysels & Arthur Sinko & Rossen Valkanov, 2007. "MIDAS Regressions: Further Results and New Directions," Econometric Reviews, Taylor & Francis Journals, vol. 26(1), pages 53-90.
    2. Bańbura, Marta & Giannone, Domenico & Modugno, Michele & Reichlin, Lucrezia, 2013. "Now-Casting and the Real-Time Data Flow," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 195-237, Elsevier.
    3. Knut Are Aastveit & Karsten R. Gerdrup & Anne Sofie Jore & Leif Anders Thorsrud, 2014. "Nowcasting GDP in Real Time: A Density Combination Approach," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(1), pages 48-68, January.
    4. Kuzin, Vladimir & Marcellino, Massimiliano & Schumacher, Christian, 2011. "MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the euro area," International Journal of Forecasting, Elsevier, vol. 27(2), pages 529-542.
    5. Eklund, Jana & Kapetanios, George, 2008. "A review of forecasting techniques for large datasets," National Institute Economic Review, National Institute of Economic and Social Research, vol. 203, pages 109-115, January.
    6. Stratford, Kate, 2013. "Nowcasting world GDP and trade using global indicators," Bank of England Quarterly Bulletin, Bank of England, vol. 53(3), pages 233-242.
    7. Kapetanios, George & Labhard, Vincent & Price, Simon, 2008. "Forecast combination and the Bank of England's suite of statistical forecasting models," Economic Modelling, Elsevier, vol. 25(4), pages 772-792, July.
    8. David Hendry & Grayham E. Mizon, 2012. "Forecasting from Structural Econometric Models," Economics Series Working Papers 597, University of Oxford, Department of Economics.
    9. Eklund, Jana & Kapetanios, George, 2008. "A review of forecasting techniques for large datasets," National Institute Economic Review, Cambridge University Press, vol. 203, pages 109-115, January.
    10. Barry Williams, 2009. "Methods Explained: The quarterly alignment adjustment," Economic & Labour Market Review, Palgrave Macmillan;Office for National Statistics, vol. 3(12), pages 78-80, December.
    11. Burgess, Stephen & Fernandez-Corugedo, Emilio & Groth, Charlotta & Harrison, Richard & Monti, Francesca & Theodoridis, Konstantinos & Waldron, Matt, 2013. "The Bank of England's forecasting platform: COMPASS, MAPS, EASE and the suite of models," Bank of England working papers 471, Bank of England.
    12. Hackworth, Christopher & Radia, Amar & Roberts, Nyssa, 2013. "Understanding the MPC’s forecast performance since mid-2010," Bank of England Quarterly Bulletin, Bank of England, vol. 53(4), pages 336-350.
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    Cited by:

    1. Drudi, Francesco & Moench, Emanuel & Holthausen, Cornelia & Weber, Pierre-François & Ferrucci, Gianluigi & Setzer, Ralph & Adao, Bernardino & Dées, Stéphane & Alogoskoufis, Spyros & Téllez, Mar Delgad, 2021. "Climate change and monetary policy in the euro area," Occasional Paper Series 271, European Central Bank.
    2. Pinkwart, Nicolas, 2018. "Short-term forecasting economic activity in Germany: A supply and demand side system of bridge equations," Discussion Papers 36/2018, Deutsche Bundesbank.
    3. Carlos León & Fabio Ortega, 2018. "Nowcasting Economic Activity with Electronic Payments Data: A Predictive Modeling Approach," Revista de Economía del Rosario, Universidad del Rosario, vol. 21(2), pages 381-407, December.
    4. Gary Koop & Stuart McIntyre & James Mitchell, 2020. "UK regional nowcasting using a mixed frequency vector auto‐regressive model with entropic tilting," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(1), pages 91-119, January.
    5. Schumacher, Christian, 2016. "A comparison of MIDAS and bridge equations," International Journal of Forecasting, Elsevier, vol. 32(2), pages 257-270.
    6. Emilian DOBRESCU, 2020. "Self-fulfillment degree of economic expectations within an integrated space: The European Union case study," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 5-32, December.
    7. Barnett, Alina & Batten, Sandra & Chiu, Adrian & Franklin, Jeremy & Sebastia-Barriel, Maria, 2014. "The UK productivity puzzle," Bank of England Quarterly Bulletin, Bank of England, vol. 54(2), pages 114-128.
    8. 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.
    9. Bholat, David, 2015. "Big data and central banks," Bank of England Quarterly Bulletin, Bank of England, vol. 55(1), pages 86-93.

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