Abstract
Recent decades have seen advances in using econometric methods to produce more timely and higher frequency estimates of economic activity at the national level, enabling better tracking of the economy in real-time. These advances have not generally been replicated at the sub-national level, likely because of the empirical challenges that nowcasting at a regional level presents, notably, the short time series of available data, changes in data frequency over time, and the hierarchical structure of the data. This paper develops a mixed-frequency Bayesian VAR model to address common features of the regional nowcasting context, using an application to regional productivity in the UK. We evaluate the contribution that different features of our model provide to the accuracy of point and density nowcasts, in particular, the role of hierarchical aggregation constraints. We show that these aggregation constraints, imposed in stochastic form, play a crucial role in delivering improved regional nowcasts; they prove more important than adding region-specific predictors when the equivalent national data are known, but not when this aggregate is unknown.
Original language | English |
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Pages (from-to) | 626-640 |
Number of pages | 15 |
Journal | International Journal of Forecasting |
Volume | 40 |
Issue number | 2 |
Early online date | 21 May 2022 |
DOIs | |
Publication status | Published - 30 Jun 2024 |
Funding
We thank an associate editor and two anonymous referees for helpful comments. This research has been funded by the Office for National Statistics as part of the Economic Statistics Centre of Excellence (ESCoE) research program . The views expressed herein are those of the authors and not necessarily those of the Federal Reserve Bank of Cleveland or the Federal Reserve System. All authors are Research Associates at the Economic Statistics Centre of Excellence.
Keywords
- regional data
- mixed frequency
- nowcasting
- Bayesian methods
- real-time data
- vector autoregressions