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Regional Output Growth in the United Kingdom: More Timely and Higher Frequency Estimates, 1970-2017

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  • Gary Koop
  • Stuart McIntyre
  • James Mitchell
  • Aubrey Poon

Abstract

Output growth estimates for the regions of the UK are currently published at the annual frequency only and are released with a long delay. Regional economists and policymakers would benefit from having higher frequency and more timely estimates. In this paper we develop a mixed frequency Vector Autoregressive (MF-VAR) model and use it to produce estimates of quarterly regional output growth. Temporal and cross-sectional restrictions are imposed in the model to ensure that our quarterly regional estimates are consistent with the annual regional observations and the observed quarterly UK totals. We use a machine learning method based on the hierarchical Dirichlet-Laplace prior to ensure optimal shrinkage and parsimony in our over-parameterised MF-VAR. Thus,this paper presents a new, regional quarterly database of nominal and real Gross Value Added dating back to 1970. We describe how we update and evaluate these estimates on an ongoing, quarterly basis to publish online (at www.escoe.ac.uk/regionalnowcasting) more timely estimates of regional economic growth. We illustrate how the new quarterly data can be used to contribute to our historical understanding of business cycle dynamics and connectedness between regions.

Suggested Citation

  • Gary Koop & Stuart McIntyre & James Mitchell & Aubrey Poon, 2018. "Regional Output Growth in the United Kingdom: More Timely and Higher Frequency Estimates, 1970-2017," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2018-14, Economic Statistics Centre of Excellence (ESCoE).
    • Handle: RePEc:nsr:escoed:escoe-dp-2018-14
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    Cited by:

    1. Koop, Gary & McIntyre, Stuart & Mitchell, James & Poon, Aubrey, 2020. "Reconciled Estimates of Monthly GDP in the US," EMF Research Papers 37, Economic Modelling and Forecasting Group.
    2. Huber, Florian & Koop, Gary & Onorante, Luca & Pfarrhofer, Michael & Schreiner, Josef, 2023. "Nowcasting in a pandemic using non-parametric mixed frequency VARs," Journal of Econometrics, Elsevier, vol. 232(1), pages 52-69.
    3. Chernis, Tony & Cheung, Calista & Velasco, Gabriella, 2020. "A three-frequency dynamic factor model for nowcasting Canadian provincial GDP growth," International Journal of Forecasting, Elsevier, vol. 36(3), pages 851-872.
    4. Marianne Sensier & Fiona Devine, 2019. "Understanding Regional Economic Performance and Resilience in the UK: Trends Since the Global Financial Crisis," Economics Discussion Paper Series 1912, Economics, The University of Manchester.
    5. Gefang, Deborah & Koop, Gary & Poon, Aubrey, 2020. "Computationally efficient inference in large Bayesian mixed frequency VARs," Economics Letters, Elsevier, vol. 191(C).
    6. Luke Mosley & Idris A. Eckley & Alex Gibberd, 2022. "Sparse temporal disaggregation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 2203-2233, October.
    7. Gary Koop & Stuart McIntyre & James Mitchell & Aubrey Poon, 2020. "Regional output growth in the United Kingdom: More timely and higher frequency estimates from 1970," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(2), pages 176-197, March.
    8. Meredith M. Paker, 2020. "The Jobless Recovery After the 1980-1981 UK Recession," Oxford Economic and Social History Working Papers _182, University of Oxford, Department of Economics.
    9. 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.
    10. Luke Mosley & Idris Eckley & Alex Gibberd, 2021. "Sparse Temporal Disaggregation," Papers 2108.05783, arXiv.org, revised Oct 2022.

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    More about this item

    Keywords

    Regional data; Mixed frequency; Nowcasting; Bayesian methods; Realtime data; Vector autoregressions;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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