Large-scale administrative data held by HM Revenue and Customs (HMRC) might be used to enhance the accuracy of early estimates of GDP and its components. In practice, incorporating these sources into the production of the National Accounts involves addressing a range of statistical issues. In this project we investigated methods for making use of companies’ Value Added Tax (VAT) returns in the calculation of early GDP estimates. As more data become available, we are investigating the stability of our modelling framework and whether ‘Pay As You Earn’ (PAYE) Real Time Information (RTI) data might further increase the accuracy of early GDP estimates.
Administrative data such as companies’ VAT records provide potential source material for producing National Accounts. Output figures are currently compiled from turnover data collected by a monthly survey of businesses, the Monthly Business Survey (MBS). This enumerates all large firms and a sample of small firms. VAT returns, made by nearly all firms with an annual turnover larger than £83,000, similarly include data on firm turnover. This project has shown how the VAT returns for small firms can be used to produce turnover figures which can be used in the calculation of GDP. We consider if the model provides consistent results as more data gradually become available, and, consequently, if the model is stable enough to be implemented in ONS production processes. We are also exploring the joint modelling of PAYE RTI data (which relate directly to one component of value added) and gross sales VAT data. This will allow us to enhance preliminary estimates of both income and output, and so improve the accuracy of our estimates of UK GDP.
We obtained vintages of VAT data from ONS for small firms, industry by industry, with the attribution issues resolved as far as possible and grossed up to represent the whole of each industry. ONS also provided MBS data for small and large firms separately by industry. We used the turnover data for large firms to inform the temporal disaggregation and nowcasting of the data for small firms. Initially we explored least-squares methods of temporal disaggregation. These, however, delivered very erratic estimates. As an alternative, we developed a state-space model to handle jointly the issues of temporal disaggregation and seasonal adjustment. We estimated this model in partnership with one for large firms in the MBS as a set of seemingly unrelated time series equations, with correlations in the disturbances estimated in the VAT and MBS models. We accounted for measurement errors in the VAT figures by devising methods to deal with the difficult task of separating the irregular changes in the seasonally adjusted figures from these measurement errors. In on-going research, we are examining the stability of our modelling framework in a nowcasting context. By estimating the state space model over an extended sample it is possible to establish the stability of both its parameters and of the interpolands that it generates.
Our research has shown how VAT returns for small companies can be used to improve the information content of early GDP estimates. The methods that we have developed provide a means by which ONS can make full and efficient use of the VAT data accruing in real time as a basis for producing output data.
Labonne, P. and Weale, M. (2022) ‘Nowcasting in the presence of large measurement errors and revisions‘ ESCoE Discussion Paper Series ESCoE DP 2022-05, 9 Mar 2022
Labonne, P. (2021) ‘Nowcasting in the Presence of Large Measurement Errors and Revisions’ ESCoE Conference on Economic Measurement 2021 Poster Exhibition, 11-13 May 2021. Poster Presentation.
Labonne, P. and Weale, M. (2020) “Temporal disaggregation of overlapping noisy quarterly data: estimation of monthly output from UK value-added tax data” Journal of the Royal Statistical Society Statistics in Society Series A, Vol 183, Issue 3 Wiley. https://doi.org/10.1111/rssa.12568
Labonne, P. and Weale, M. (2019) “Nowcasting GDP from VAT returns in the UK” Workshop on Time Series Methods for Official Statistics, 26-27 September 2019 Paris, The Seasonal Adjustment Centre of Excellence (SACE), in collaboration with Eurostat and OECD.