Temporal Disaggregation of Overlapping Noisy Quarterly Data: Estimation of Monthly Output from Uk Value-added Tax Data

Abstract

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.

Capturing GDP nowcast uncertainty in real time

“…The temporal aggregation strategies for modelling jointly quarterly and monthly observations and modelling rolling quarterly observations (quarterly observations observed monthly) are essentially the same; in both cases it is necessary to go back to the underlying monthly model. Labonne and Weale (2020) show that when rolling quarterly series are not subject to important measurement errors it is possible to interpolate the monthly path very precisely with ( 14). Hence there should be little loss of information when using rolling quarterly observations instead of monthly observations.…”

Sparse Temporal Disaggregation

“…See Pavía- Miralles et al (2010) for an extensive literature review of disaggregation procedures and Chen (2007) for an empirical comparison using 60 series of annual data from national accounts. More recent work of temporal disaggregation includes Labonne and Weale (2020) who derive monthly estimates of business sector output in the United Kingdom from rolling quarterly VAT returns by employing an unobserved components model to accommodate measurement noise.…”

Sparse Temporal Disaggregation

“…See Pavía- Miralles et al (2010) for an extensive literature review of disaggregation procedures and Chen (2007) for an empirical comparison using 60 series of annual data from national accounts. More recent work of temporal disaggregation includes Labonne and Weale (2020) who derive monthly estimates of business sector output in the UK from rolling quarterly VAT returns by employing an unobserved components model to accommodate measurement noise.…”