UK Regional Forecasting using Mixed Frequency Big Data

The proposed research will involve the development of models for improving regional UK economic nowcasting and forecasting in an era of mixed frequency Big Data. These will ultimately assist economists in government and the private sector in implementing policy decisions by providing quarterly nowcasts and forecasts.
Currently Gross Value Added (GVA) for the 13 regions in the UK is released only once a year with a long delay. Thus, for long periods, regional policymakers are making decisions without an accurate knowledge of what current GVA is, much less what it will be in the near future. However, many potential predictors for regional GVA are released on a more frequent and timely basis. This frequency mis-match offers the potential for updating nowcasts or forecasts more regularly than is current practice. The main impact of the proposed research will arise from the desire by policymakers to have the timely and accurate forecasts that I will produce.
I plan on beginning with a relatively small data set, working with GVA data for 13 UK regions and GVA for the UK as a whole (which is released on a quarterly basis and with much less delay than regional GVA data), but will expand to include other predictors. These include variables available for the UK as a whole (e.g. industrial production, unemployment and financial variables) and those available at the regional level (e.g. labour market variables and business surveys). This implies I will be working with a huge number of variables and, thus, will lead to Big Data issues. That is, even working with only regional and UK GVA data will involve an econometric model with 14 variables. Each extra UK variable will add one to the number of variables in the model and each extra regional variable will add 13 variables. The goal is to work with forecasting models involving up to 100 variables. Vector autoregressive (VAR) models are the gold standard forecasting model used for macroeconomic forecasting and, beginning with the pioneering paper of Banbura et al. (2010), the research frontier has moved forward to deal with large VARs involving VARs of this size. I plan on using such large VAR methods to produce regional forecasts and nowcasts.
But regional forecasting involves another issue that is not addressed in the large VAR literature. This is the fact that the data is of mixed frequency. That is, regional GVA is available at an annual frequency, UK GVA at the quarterly frequency and other predictors (e.g. industrial production and many business surveys) are available at the monthly frequency. There is a new, cutting edge, literature on mixed frequency econometrics that I plan on extending in this work. The UK regional forecasting context differs from the existing literature in several important ways. First, there are many lower frequency variables (the regional GVA variables) than usual. Second, the fact that GVA for the UK regions adds up to UK GVA provides an extra cross-sectional restriction which should help improve forecasts. I plan on addressing these issues in my proposed research.