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2024 (English)In: Journal of the Royal Statistical Society: Series A (Statistics in Society), ISSN 0964-1998, E-ISSN 1467-985X, Vol. 187, no 2, p. 477-495Article in journal (Refereed) Published
Abstract [en]
Interest in regional economic issues coupled with advances in administrative data is driving the creation of new regional economic data. Many of these data series could be useful for nowcasting regional economic activity, but they suffer from a short (albeit constantly expanding) time series which makes incorporating them into nowcasting models problematic. Regional nowcasting is already challenging because the release delay on regional data tends to be greater than that at the national level, and 'short' data imply a 'ragged edge' at both the beginning and the end of regional data sets, which adds a further complication. In this paper, via an application to the UK, we investigate various ways of including a wide range of short data into a regional mixed-frequency vector autoregression (MF-VAR) model. These short data include hitherto unexploited regional value-added tax turnover data. We address the problem of the two ragged edges by estimating regional factors using different missing data algorithms that we then incorporate into our MF-VAR model. We find that nowcasts of regional output growth are generally improved when we condition them on the factors, but only when the regional nowcasts are produced before the national (UK-wide) output growth data are published.
Place, publisher, year, edition, pages
Oxford University Press, 2024
Keywords
Bayesian methods, factors, missing data, mixed-frequency data, regional data, vector autoregressions, C32, C53, E37
National Category
Economics
Identifiers
urn:nbn:se:oru:diva-110038 (URN)10.1093/jrsssa/qnad130 (DOI)001105758200001 ()2-s2.0-85190499587 (Scopus ID)
2023-12-052023-12-052025-01-20Bibliographically approved