Temporal disaggregation of overlapping noisy quarterly data: estimation of monthly output from UK value‐added tax data

Paul Labonne and Martin Weale

Journal of the Royal Statistical Society Series A, 2020, vol. 183, issue 3, 1211-1230

Abstract: The paper derives monthly estimates of business sector output in the UK from rolling quarterly value‐added tax based turnover data. The administrative nature of the value‐added tax data implies that their use could ultimately yield a more precise and granular picture of output across the economy. However, they show two particular features which complicate their exploitation: they are overlapping and subject to substantial noise. This motivates our choice of a multivariate unobserved components model for filtering and disaggregating temporally the aggregate figures. After illustrating our method by using one industry as a case‐study, we estimate monthly seasonally adjusted gross output figures for the 75 industries for which the data are available. Our results show material differences from the existing output profile.

Date: 2020
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