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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.
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Capturing GDP nowcast uncertainty in real time
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