IDEAS home Printed from https://ideas.repec.org/a/boe/qbullt/0132.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.bankofengland.co.uk/-/media/boe/files/quarterly-bulletin/2014/nowcasting-uk-gdp-growth.pdf?la=en&hash=609B6B621A616C64D29B8CCC0A528882E8DB0059
    File Function: Full text
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. 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.
    4. Jana Eklund & George Kapetanios, 2008. "A review of forecasting techniques for large datasets," National Institute Economic Review, National Institute of Economic and Social Research, vol. 203(1), pages 109-115, January.
    5. 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.
    6. 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.
    7. David Hendry & Grayham E. Mizon, 2012. "Forecasting from Structural Econometric Models," Economics Series Working Papers 597, University of Oxford, Department of Economics.
    8. 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.
    9. repec:bny:wpaper:0003 is not listed on IDEAS
    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.
    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. 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. 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.
    5. 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.
    6. 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.
    7. Schumacher, Christian, 2016. "A comparison of MIDAS and bridge equations," International Journal of Forecasting, Elsevier, vol. 32(2), pages 257-270.
    8. 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.
    9. Bholat, David, 2015. "Big data and central banks," Bank of England Quarterly Bulletin, Bank of England, vol. 55(1), pages 86-93.

    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. Chudik, Alexander & Grossman, Valerie & Pesaran, M. Hashem, 2016. "A multi-country approach to forecasting output growth using PMIs," Journal of Econometrics, Elsevier, vol. 192(2), pages 349-365.
    2. Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Economics Working Papers ECO2013/02, European University Institute.
    3. Baumeister, Christiane & Guérin, Pierre, 2021. "A comparison of monthly global indicators for forecasting growth," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1276-1295.
    4. Alain Galli & Christian Hepenstrick & Rolf Scheufele, 2019. "Mixed-Frequency Models for Tracking Short-Term Economic Developments in Switzerland," International Journal of Central Banking, International Journal of Central Banking, vol. 15(2), pages 151-178, June.
    5. Tóth, Peter, 2014. "Malý dynamický faktorový model na krátkodobé prognózovanie slovenského HDP [A Small Dynamic Factor Model for the Short-Term Forecasting of Slovak GDP]," MPRA Paper 63713, University Library of Munich, Germany.
    6. Bec, Frédérique & Mogliani, Matteo, 2015. "Nowcasting French GDP in real-time with surveys and “blocked” regressions: Combining forecasts or pooling information?," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1021-1042.
    7. Foroni, Claudia & Guérin, Pierre & Marcellino, Massimiliano, 2015. "Markov-switching mixed-frequency VAR models," International Journal of Forecasting, Elsevier, vol. 31(3), pages 692-711.
    8. Knotek, Edward S. & Zaman, Saeed, 2019. "Financial nowcasts and their usefulness in macroeconomic forecasting," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1708-1724.
    9. Jos Jansen & Jasper de Winter, 2016. "Improving model-based near-term GDP forecasts by subjective forecasts: A real-time exercise for the G7 countries," DNB Working Papers 507, Netherlands Central Bank, Research Department.
    10. Tony Chernis & Rodrigo Sekkel, 2018. "Nowcasting Canadian Economic Activity in an Uncertain Environment," Discussion Papers 18-9, Bank of Canada.
    11. Boneva, Lena & Fawcett, Nicholas & Masolo, Riccardo M. & Waldron, Matt, 2019. "Forecasting the UK economy: Alternative forecasting methodologies and the role of off-model information," International Journal of Forecasting, Elsevier, vol. 35(1), pages 100-120.
    12. Zhang, Qin & Ni, He & Xu, Hao, 2023. "Nowcasting Chinese GDP in a data-rich environment: Lessons from machine learning algorithms," Economic Modelling, Elsevier, vol. 122(C).
    13. Schumacher Christian, 2011. "Forecasting with Factor Models Estimated on Large Datasets: A Review of the Recent Literature and Evidence for German GDP," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 231(1), pages 28-49, February.
    14. Smith Paul, 2016. "Nowcasting UK GDP during the depression," Working Papers 1606, University of Strathclyde Business School, Department of Economics.
    15. Liu, Ying & Wen, Long & Liu, Han & Song, Haiyan, 2024. "Predicting tourism recovery from COVID-19: A time-varying perspective," Economic Modelling, Elsevier, vol. 135(C).
    16. Fawcett, Nicholas & Koerber, Lena & Masolo, Riccardo & Waldron, Matthew, 2015. "Evaluating UK point and density forecasts from an estimated DSGE model: the role of off-model information over the financial crisis," Bank of England working papers 538, Bank of England.
    17. João C. Claudio & Katja Heinisch & Oliver Holtemöller, 2020. "Nowcasting East German GDP growth: a MIDAS approach," Empirical Economics, Springer, vol. 58(1), pages 29-54, January.
    18. Winkelried, Diego, 2012. "Predicting quarterly aggregates with monthly indicators," Working Papers 2012-023, Banco Central de Reserva del Perú.
    19. Marina Diakonova & Luis Molina & Hannes Mueller & Javier J. Pérez & Cristopher Rauh, 2022. "The information content of conflict, social unrest and policy uncertainty measures for macroeconomic forecasting," Working Papers 2232, Banco de España.
    20. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2015. "Realtime nowcasting with a Bayesian mixed frequency model with stochastic volatility," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(4), pages 837-862, October.

    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:boe:qbullt:0132. 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: Publications Group (email available below). General contact details of provider: https://edirc.repec.org/data/boegvuk.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.