Regional Output Growth in the United Kingdom: More Timely and Higher Frequency Estimates, 1970-2017
Gary Koop (),
Stuart McIntyre (),
James Mitchell () and
Aubrey Poon ()
No ESCoE DP-2018-14, Economic Statistics Centre of Excellence (ESCoE) Discussion Papers from Economic Statistics Centre of Excellence (ESCoE)
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.
Keywords: Regional data; Mixed frequency; Nowcasting; Bayesian methods; Realtime data; Vector autoregressions (search for similar items in EconPapers)
JEL-codes: C32 C53 E37 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-big, nep-ets, nep-mac and nep-ure
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Working Paper: Regional Output Growth in the United Kingdom: More Timely and Higher Frequency Estimates, 1970-2017 (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:nsr:escoed:escoe-dp-2018-14
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