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UK Real-time Macro Data Characteristics


  • Shaun Vahey
  • Tony Garratt

    () (Research RBNZ)


We characterise the relationships between preliminary and subsequent measurements for 16 commonly-used UK macroeconomic indicators drawn from two existing real-time data sets and a new nominal variable database. Most preliminary measurements are biased predictors of subsequent measurements, with some revision series affected by multiple structural breaks. To illustrate how these findings facilitate real-time forecasting, we use a vector autoregresion to generate real-time one-step-ahead probability event forecasts for 1990Q1 to 1999Q2. Ignoring the predictability in initial measurements understates considerably the probability of above trend output growth

Suggested Citation

  • Shaun Vahey & Tony Garratt, 2005. "UK Real-time Macro Data Characteristics," Computing in Economics and Finance 2005 253, Society for Computational Economics.
  • Handle: RePEc:sce:scecf5:253

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    References listed on IDEAS

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    Cited by:

    1. Alastair Cunningham & Jana Eklund & Chris Jeffery & George Kapetanios & Vincent Labhard, 2009. "A State Space Approach to Extracting the Signal From Uncertain Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(2), pages 173-180, March.
    2. Francisco Castro & Javier J. Pérez & Marta Rodríguez‐Vives, 2013. "Fiscal Data Revisions in Europe," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 45(6), pages 1187-1209, September.
    3. Anthony Garratt & Kevin Lee & Emi Mise & Kalvinder Shields, 2006. "Real Time Representation of the UK Output Gap in the Presence of Trend Uncertainty," Birkbeck Working Papers in Economics and Finance 0618, Birkbeck, Department of Economics, Mathematics & Statistics.
    4. Rusnák, Marek, 2016. "Nowcasting Czech GDP in real time," Economic Modelling, Elsevier, vol. 54(C), pages 26-39.
    5. Alfonso Mendoza Velázquez & Peter N. Smith, 2013. "Equity Returns and the Business Cycle: the Role of Supply and Demand Shocks," Manchester School, University of Manchester, vol. 81, pages 100-124, September.
    6. Jacopo Cimadomo, 2016. "Real-Time Data And Fiscal Policy Analysis: A Survey Of The Literature," Journal of Economic Surveys, Wiley Blackwell, vol. 30(2), pages 302-326, April.
    7. Jan Jacobs & Jan-Egbert Sturm, 2007. "A real-time analysis of the Swiss trade account," Money Macro and Finance (MMF) Research Group Conference 2006 167, Money Macro and Finance Research Group.
    8. Garratt, Anthony & Koop, Gary & Mise, Emi & Vahey, Shaun P., 2009. "Real-Time Prediction With U.K. Monetary Aggregates in the Presence of Model Uncertainty," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 480-491.
    9. Akram, Q. Farooq, 2011. "Policy analysis in real time using IMF's monetary model," Economic Modelling, Elsevier, vol. 28(4), pages 1696-1709, July.
    10. Dean Croushore, 2011. "Frontiers of Real-Time Data Analysis," Journal of Economic Literature, American Economic Association, vol. 49(1), pages 72-100, March.
    11. Jacobs, Jan P.A.M. & van Norden, Simon, 2011. "Modeling data revisions: Measurement error and dynamics of "true" values," Journal of Econometrics, Elsevier, vol. 161(2), pages 101-109, April.
    12. Steve Cook, 2008. "Cross-data-vintage Encompassing," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 70(s1), pages 849-865, December.
    13. Ronney Ncwadi, 2016. "Assessing Efficiency of GDP Revisions in South Africa," Journal of Education and e-Learning Research, Asian Online Journal Publishing Group, vol. 3(2), pages 72-77.
    14. Alastair Cunningham & Chris Jeffery & George Kapetanios & Vincent Labhard, 2007. "A State Space Approach To The Policymaker's Data Uncertainty Problem," Money Macro and Finance (MMF) Research Group Conference 2006 168, Money Macro and Finance Research Group.
    15. Anthony Garratt & Gary Koop & ShaunP. Vahey, 2008. "Forecasting Substantial Data Revisions in the Presence of Model Uncertainty," Economic Journal, Royal Economic Society, vol. 118(530), pages 1128-1144, July.
    16. Golinelli, Roberto & Parigi, Giuseppe, 2005. "Short-Run Italian GDP Forecasting and Real-Time Data," CEPR Discussion Papers 5302, C.E.P.R. Discussion Papers.
    17. Garratt, Anthony & Lee, Kevin & Mise, Emi & Shields, Kalvinder, 2009. "Real time representation of the UK output gap in the presence of model uncertainty," International Journal of Forecasting, Elsevier, vol. 25(1), pages 81-102.
    18. Juan Manuel Julio Román, 2011. "Modeling Data Revisions," BORRADORES DE ECONOMIA 007929, BANCO DE LA REPÚBLICA.
    19. Pierre Siklos, 2006. "What Can We Learn from Comprehensive Data Revisions for Forecasting Inflation: Some US Evidence," Working Papers eg0049, Wilfrid Laurier University, Department of Economics, revised 2006.
    20. Jens R Clausen & Bianca Clausen, 2010. "Simulating Inflation Forecasting in Real-Time; How Useful Is a Simple Phillips Curve in Germany, the UK, and the US?," IMF Working Papers 10/52, International Monetary Fund.
    21. Thomas A. Knetsch & Hans-Eggert Reimers, 2009. "Dealing with Benchmark Revisions in Real-Time Data: The Case of German Production and Orders Statistics," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(2), pages 209-235, April.
    22. Dean Croushore, 2008. "Revisions to PCE inflation measures: implications for monetary policy," Working Papers 08-8, Federal Reserve Bank of Philadelphia.
    23. Cath Sleeman, 2006. "Analysis of revisions to quarterly GDP - a real-time database," Reserve Bank of New Zealand Bulletin, Reserve Bank of New Zealand, vol. 69, pages 1-44., March.
    24. Kevin Lee & Nilss Olekalns & Kalvinder Shields & Zheng Wang, 2012. "Australian Real-Time Database: An Overview and an Illustration of its Use in Business Cycle Analysis," The Economic Record, The Economic Society of Australia, vol. 88(283), pages 495-516, December.
    25. Kevin Lee, Nilss Olekalns, Kalvinder Shields and Zheng Wang, 2011. "The Australian Real?Time Datbase: An Overview and an Illustration of its Use in Business Cycle Analysis," Department of Economics - Working Papers Series 1132, The University of Melbourne.

    More about this item


    real-time data; structural breaks; probability event;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • E00 - Macroeconomics and Monetary Economics - - General - - - General


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