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Nowcasting Scottish GDP Growth

Author

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  • Grant Allan
  • Gary Koop

    (Department of Economics, University of Strathclyde, United Kingdom; The Rimini Centre for Economic Analysis, Italy)

  • Stuart McIntyre
  • Paul Smith

Abstract

The delays in the release of macroeconomic variables such as GDP mean that policymakers do not know their current values. Thus, nowcasts, which are estimates of current values of macroeconomic variables, are becoming increasingly popular. This paper takes up the challenge of nowcasting Scottish GDP growth. Nowcasting in Scotland, currently a government office region within the United Kingdom, is complicated due to data limitations. For instance, key nowcast predictors such as industrial production are unavailable. Accordingly, we use data on some non-traditional variables and investigate whether UK aggregates can help nowcast Scottish GDP growth. Such data limitations are shared by many other sub-national regions, so we hope this paper can provide lessons for other regions interested in developing nowcasting models.

Suggested Citation

  • Grant Allan & Gary Koop & Stuart McIntyre & Paul Smith, 2014. "Nowcasting Scottish GDP Growth," Working Paper series 41_14, Rimini Centre for Economic Analysis.
    • Handle: RePEc:rim:rimwps:41_14
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    References listed on IDEAS

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    1. Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Working Paper 2013/06, Norges Bank.
    2. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2008. "Nowcasting: The real-time informational content of macroeconomic data," Journal of Monetary Economics, Elsevier, vol. 55(4), pages 665-676, May.
    3. Camacho, Maximo & Perez-Quiros, Gabriel & Poncela, Pilar, 2013. "Short-term Forecasting for Empirical Economists: A Survey of the Recently Proposed Algorithms," Foundations and Trends(R) in Econometrics, now publishers, vol. 6(2), pages 101-161, November.
    4. 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.
    5. Domenico Giannone & Lucrezia Reichlin & David Small, 2008. "Nowcasting: the real time informational content of macroeconomic data releases," ULB Institutional Repository 2013/6409, ULB -- Universite Libre de Bruxelles.
    6. Domenico Giannone & Lucrezia Reichlin & David H. Small, 2005. "Nowcasting GDP and inflation: the real-time informational content of macroeconomic data releases," Finance and Economics Discussion Series 2005-42, Board of Governors of the Federal Reserve System (U.S.).
    7. 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.
    8. Claudia Foroni & Massimiliano Marcellino & Christian Schumacher, 2015. "Unrestricted mixed data sampling (MIDAS): MIDAS regressions with unrestricted lag polynomials," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(1), pages 57-82, January.
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    Cited by:

    1. Knotek, Edward S. & Zaman, Saeed, 2023. "Real-time density nowcasts of US inflation: A model combination approach," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1736-1760.
    2. María Gil & Danilo Leiva-Leon & Javier J. Pérez & Alberto Urtasun, 2019. "An application of dynamic factor models to nowcast regional economic activity in Spain," Occasional Papers 1904, Banco de España.

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