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

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. Ho...

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Bibliographic Details
Published inJournal of the Royal Statistical Society. Series A, Statistics in society Vol. 183; no. 3; pp. 1211 - 1230
Main Authors Labonne, Paul, Weale, Martin
Format Journal Article
LanguageEnglish
Published Oxford Wiley 01.06.2020
Oxford University Press
Subjects
Administrative data
Exploitation
Monthly gross domestic product
Noise
Original Articles
State space models
Taxation
Temporal disaggregation
Unobserved components models
United Kingdom > UK
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Summary: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.
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ISSN:0964-1998
1467-985X
DOI:10.1111/rssa.12568