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Nowcasting Using Mixed Frequency Methods: An Application to the Scottish Economy

Author

Listed:
  • Grant Allan

    (University of Strathclyde
    University of Strathclyde)

  • Gary Koop

    (University of Strathclyde)

  • Stuart McIntyre

    (University of Strathclyde
    University of Strathclyde)

  • Paul Smith

    (Markit Ltd)

Abstract

The delays in the release of key economic variables mean that policymakers do not know their current values. Quickly produced, high frequency, indicators are essential in understanding economic performance in a timely fashion. Thus, there is a need for nowcasts, which are estimates of the current values of such variables (e.g. GDP, employment, etc.). This paper nowcasts economic growth in Scotland. Nowcasting the Scottish economy is complicated because the government statistical agency treats Scotland as a region within the UK. This raises issues of data timeliness and availability. For instance, key nowcast predictors such as industrial production are unavailable at the sub-national level. Accordingly, we use data on some non-traditional variables and investigate whether UK aggregates, and indicators for neighbouring regions of the UK, can help nowcast Scottish GDP growth. Similar considerations hold for other regions in other countries. Thus, we show that these models and methods can be successfully adapted for use in a regional setting, and so produce timely macroeconomic indicators for other regional economies.

Suggested Citation

  • Grant Allan & Gary Koop & Stuart McIntyre & Paul Smith, 2019. "Nowcasting Using Mixed Frequency Methods: An Application to the Scottish Economy," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(1), pages 12-45, September.
    • Handle: RePEc:spr:sankhb:v:81:y:2019:i:1:d:10.1007_s13571-018-0181-2
    DOI: 10.1007/s13571-018-0181-2
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    References listed on IDEAS

    as
    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.
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    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.
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    7. Reichlin, Lucrezia & Giannone, Domenico & Small, David, 2005. "Nowcasting GDP and Inflation: The Real Time Informational Content of Macroeconomic Data Releases," CEPR Discussion Papers 5178, C.E.P.R. Discussion Papers.
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    Cited by:

    1. Arnab Bhattacharjee & Tapabrata Maiti, 2019. "P. C. Mahalanobis in the Context of Current Econometrics Research," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(1), pages 1-11, September.

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    More about this item

    Keywords

    Nowcasting; Mixed frequency data; Regional economics;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • O18 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Urban, Rural, Regional, and Transportation Analysis; Housing; Infrastructure
    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes
    • P20 - Political Economy and Comparative Economic Systems - - Socialist and Transition Economies - - - General

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