These experimental estimates are produced using an econometric model created as part of the Economic Statistics Centre of Excellence (ESCoE) research project, Regional Nowcasting in the UK, an overview of which is provided in the accompanying article.
The accompanying article provides a historic comparison with the Office for National Statistics’ previously published, GDP, UK regions and countries quarterly estimates, which these estimates are an early indication of.
All nine English regions, Wales, Scotland, and Northern Ireland are estimated to have seen a positive quarter-on-previous-quarter growth in gross value added in Quarter 3 (July to Sept) 2021, ranging from 0.9% to 1.5%.
In all regions and countries, the estimated growth in Quarter 3 2021 was lower than in Quarter 2 (Apr to June) 2021.
Data within this release are presented as quarter-on-previous-quarter growth rates.
These estimates of seasonally adjusted growth are modelled from a variety of data sources, including the first estimate of UK gross domestic product (GDP) for Quarter 3 (Jul to Sept) 2021. The regional estimates are subject to a degree of uncertainty expressed as confidence intervals (see Section 7).
Figure 1: For all regions the estimated growth in gross value added (GVA) in Quarter 3 (July to Sept) 2021 was lower than in Quarter 2 (Apr to June) 2021
Model-based estimates of regional GVA growth for the English regions, Wales, Scotland, and Northern Ireland for Quarter 2 (Apr to June) 2021 and Quarter 3 2021
This is based on a 95% Confidence Interval Root Mean Square Error (RMSE), relative to the published estimate.
This figure also shows the updated estimates for Quarter 2 2021 following the revision to Quarter 2 2021 UK GDP, published in the first estimate of UK gross domestic product (GDP) for Quarter 3 2021.
Download this chart
The model-based estimates of regional GVA indicate that the East Midlands, East of England, London, South East, South West and West Midlands had growth of greater than the UK-wide figure in Quarter 3 2021 (Figure 1), the highest of which were the East of England, London and South West all at 1.5%.
The model estimates that the North East, Yorkshire and The Humber, North West, Wales, Scotland and Northern Ireland have growth below the UK GVA, the lowest of which was the North East at 0.9%.Nôl i'r tabl cynnwys
The model used to produce the estimates utilises a mixed-frequency Vector Autoregressive (MF-VAR). This is described in technical detail in UK Regional Nowcasting using a mixed frequency Vector Autoregressive Model.
The Economic Statistics Centre of Excellence (ESCoE) nowcast model, in essence, takes the latest figure for UK gross domestic product (GDP) growth and then forms an estimate for each region of England and Wales based on the four factors noted, such that the weighted sum of regional changes is consistent with the change in aggregate UK GDP.
There are four main methodological processes in the model, which are used to create the estimates:
estimated historical relationships between regional growth and UK growth (this reflects how sensitive regional growth is to UK growth)
estimated historical relationships between the growth of particular regions (this captures how growth in region x has translated into growth in region y)
estimated historical relationships within the regions (this captures the persistence of regional growth from one quarter to the next)
estimated historical relationships between other macroeconomic variables and regional growth (for example, how oil price changes can have a large impact on all of the regions, particularly with regards to the additional UK quarterly macroeconomic variables, so in this example there can be substantial increases in the connection between measures for the oil price and the exchange rate)
Further information is available on the ESCoE website.Nôl i'r tabl cynnwys
Model-based early estimates of regional Gross Value Added (GVA) in the regions of England, Wales, Scotland, and Northern Ireland
Dataset | Released 29 November 2021
Experimental model-based estimates of quarterly regional gross value added (GVA) output for the nine English regions (North East, North West, Yorkshire and The Humber, East Midlands, West Midlands, East of England, London, South East, and South West), Wales, Scotland, and Northern Ireland.
Model-based regional gross value added (GVA) revisions triangle
Dataset | Released 29 November 2021
Model-based regional GVA estimates based on running the model that transitioned from ESCoE to ONS in “real time” from Quarter 2 2019.
Mixed-frequency vector autoregressive (MF-VAR)
A MF-VAR approach estimates the set of regressions (for example, one for each variable in the model) as a system. This allows us to take advantage of the interactions between variables to improve the fit of the model. In this case, we expect there to be correlations between the gross domestic product (GDP) growth across regions, so a MF-VAR framework enables this information to be used rather than if we had a separate forecast model for each region. The explanatory variables in each regression are past values of all the other variables in the model.
Gross value added (GVA)
GVA is the value of an industry's outputs less the value of intermediate inputs used in the production process.
Output approach to GDP
When using the output approach to measuring GDP, we are actually estimating the contribution of each industry or producer by using GVA at basic prices; put simply, the value of a unit's outputs less the value of inputs used in the production process to produce the outputs. The basic price is the amount that the producer receives for a unit of a good or service that is produced. As such, it includes any subsidies that are received on products but excludes any taxes that are payable on those products.
The link between GVA and GDP is: GVA at basic prices plus taxes on products less subsidies on products equals GDP at market prices (or headline GDP).Nôl i'r tabl cynnwys
These data are designated as Experimental Statistics. These are statistics that are in the testing phase.
We will continue to develop the model and invite users' views on their uses and needs for these data. We welcome feedback to help inform our development work at firstname.lastname@example.org.
Model-based estimates of regional gross value added (GVA)
This release contains modelled estimates of regional GVA, published with a delay of approximately two weeks following the Office for National Statistics (ONS) release of the first estimate of gross domestic product (GDP). The model provides regional estimates for a given quarter around five months faster than the ONS' GDP, UK regions and countries, which covers the nine English regions and Wales and is based mainly on HM Revenue and Customs (HMRC) Value Added Tax (VAT) data. The model-based estimates are published approximately 6 weeks in advance of the Northern Ireland Statistics and Research Institute's (NISRA's) estimates for the same period and approximately 2 months ahead of the Welsh Government's publication of short-term output indicators for the same period. However, they are published only around 2 weeks ahead of the Scottish Government's publication of GDP for Quarter 3 (July to Sept) 2021, therefore users should refer to their website for latest estimates.
More quality and methodology information on strengths, limitations, appropriate uses, and how the data were created is available in the accompanying article, Model-based estimates of regional Gross Value Added.
As an indication of quality for the quarterly modelled estimates, Table 1 provides approximate confidence intervals calculated from the root mean square errors (RMSE) of the model, compared with ONS' GDP, UK regions and countries Quarter 1 (Jan to Mar), 2021 published on 25 November 2021. The confidence intervals are presented for the period Quarter 2 (Apr to June) 2019 to Quarter 1 (Jan to Mar) 2021, excluding Quarters 2 and 3 2020 when GVA experienced a shock caused by the coronavirus (COVID-19) pandemic.
As an example of how to interpret the confidence intervals in Table 1, the model estimated the East of England to have increased by 1.5% in Quarter 3 2021, and the confidence interval indicates 95% confidence that the true growth will lie between 0.4% and 2.6%.
|Region||Q2 2019 to Q1 |
Q2 and Q3 2020
|East Midlands||estimate +/- 2.8%|
|East of England||estimate +/- 1.1%|
|London||estimate +/- 2.7%|
|North East||estimate +/- 1.8%|
|North West||estimate +/- 1.4%|
|Northern Ireland||estimate +/- 1.5%|
|Scotland||estimate +/- 0.9%|
|South East||estimate +/- 1.6%|
|South West||estimate +/- 1.6%|
|Wales||estimate +/- 1.2%|
|West Midlands||estimate +/- 1.8%|
|Yorkshire and The Humber||estimate +/- 2.0%|
Download this table Table 1: Confidence Interval Root Mean Square Error (RMSE), relative to the published estimate.xls .csv
The model-based estimates of quarterly regional gross domestic product (GDP) are available to approximately the same timetable as the release of the UK first estimate of GDP from published data sources of mixed frequencies. Regional economic activity, by gross domestic product is published annually. More recently, since September 2019, GDP, UK regions and countries is published quarterly. However, there still remains a delay on publication of sub-national estimates because of the data sources being less timely compared with UK estimates of gross value added (GVA).
The model has been peer reviewed as part of the Economic Statistics Centre of Excellence (ESCoE) process and methodologists at the Office for National Statistics (ONS) were involved at various stages of the project and publications.
The model will continue to be evaluated against our existing predominately Value Added Tax (VAT) based GDP, UK regions and countries estimates.
The main limitation that applies to modelling in general, and is not exclusive to this model, is mainly seen during times of economic uncertainty and extreme values. This applies to various periods of the coronavirus (COVID-19) pandemic.
Further detail on strengths and limitations can be found in the paper Model-based estimates of regional GVA: an overview.Nôl i'r tabl cynnwys
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