This paper derives monthly estimates of turnover for small and medium size businesses in the UK from rolling quarterly VAT-based turnover data. We develop a state space approach for filtering and temporally disaggregating the VAT figures, which are noisy and exhibit dynamic unobserved components. We notably derive multivariate and nonlinear methods to make use of indicator series and data in logarithms respectively. After illustrating our temporal disaggregation method and estimation strategy using an example industry, we estimate monthly seasonally adjusted figures for the seventy-five industries for which the data are available. We thus produce an aggregate series representing approximately a quarter of gross value added in the economy. We compare our estimates with those derived from the Monthly Business Survey and find that the VAT-based estimates show a different time profile and are less volatile. In addition to this empirical work our contribution to the literature on temporal disaggregation is twofold. First, we provide a discussion of the effect that noise in aggregate figures has on the estimation of disaggregated model components. Secondly, we illustrate a new temporal aggregation strategy suited for overlapping data. The technique we adopt is more parsimonious than the seminal method of Harvey and Pierse (1984) and can easily be generalised to non-overlapping data.
