Incorporating Short Data into Large Mixed-Frequency VARs for Regional Nowcasting
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 develop methods to include a wide range of short data into a regional mixed-frequency VAR model. These short data include hitherto unexploited regional VAT turnover data. We address the problem of the ragged edge at both the beginning and end of our sample by estimating regional factors using different missing data algorithms that we then incorporate into our mixed-frequency 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.
Koop, Gary, Stuart McIntyre, James Mitchell, Aubrey Poon, and Ping Wu. 2023. “Incorporating Short Data into Large Mixed-Frequency VARs for Regional Nowcasting.” Federal Reserve Bank of Cleveland, Working Paper No. 23-09. https://doi.org/10.26509/frbc-wp-202309