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

Research output: Working paperDiscussion paper

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.
LanguageEnglish
Place of PublicationLondon
Pages1-54
Number of pages54
Publication statusPublished - 20 Nov 2018

Fingerprint

Output growth
Vector autoregressive model
Vector autoregressive
Dirichlet
Data base
Connectedness
Regional economic growth
Economists
Value added
Shrinkage
Parsimony
Politicians
Business cycles
Machine learning
Learning methods

Keywords

  • regional data
  • mixed frequency
  • Nowcasting
  • Bayesian methods
  • real-time data
  • vector autoregressions

Cite this

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title = "Regional Output Growth in the United Kingdom: More Timely And Higher Frequency Estimates,1970-2017",
abstract = "Output growth estimates for the regions of the UK are currently published at theannual 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.",
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Regional Output Growth in the United Kingdom : More Timely And Higher Frequency Estimates,1970-2017. / Koop, Gary; McIntyre, Stuart; Mitchell, James; Poon, Aubrey.

London, 2018. p. 1-54.

Research output: Working paperDiscussion paper

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