Nowcasting in the presence of large measurement errors and revisions (ESCoE DP 2022-05)

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Nowcasting in the presence of large measurement errors and revisions (ESCoE DP 2022-05)

By Paul Labonne,

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This paper extends the temporal disaggregation approach of Labonne and Weale (2020) to
tackle another feature of the VAT data: the delay and highly noisy nature of the early
figures. The main contribution of this paper lies in the presentation and illustration of a
cleaning method which can deal with non-Gaussian features in the distribution of
measurement errors such as asymmetry and extreme observations.