The financial crisis has led to a change in the mix of capital and labour employed in the UK and a sharp decline in total factor productivity. This has meant that labour productivity has not recovered to any great degree since the financial crisis. We explore the role of overall and sectoral productivity in explaining the fall in labour productivity, but also question the extent to which productivity in the service sector may be measured with error. We outline the links between a constrained financial sector and a fall in overall productivity – in which intangible capital seems to play an important role – and illustrate how a financial sector providing intermediate services may act to amplify the business cycle impetus from a total factor productivity shock within the context of a calibrated model.

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.

Nowcasting methods rely on timely series related to economic growth for producing and updating estimates of GDP growth before publication of official figures. But the statistical uncertainty attached to these forecasts, which is critical to their interpretation, is only improved marginally when new data on related series become available. That is particularly problematic in times of high economic uncertainty. As a solution this paper proposes to model common factors in scale and shape parameters alongside the mixed-frequency dynamic factor model typically used for location parameters in nowcasting frameworks. Scale and shape parameters control the time-varying dispersion and asymmetry round point forecasts which are necessary to capture the increase in variance and negative skewness found in times of recessions. It is shown how cross-sectional dependencies in scale and shape parameters may be modelled in mixed-frequency settings, with a particularly convenient approximation for scale parameters in Gaussian models. The benefit of this methodology is explored using vintages of U.S. economic growth data with a focus on the economic depression resulting from the coronavirus pandemic. The results show that modelling common factors in scale and shape parameters improves nowcasting performance towards the end of the nowcasting window in recessionary episodes.