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Title: Monthly GDP estimates for the US states Authors:  Aristeidis Raftapostolos - University of Strathclyde (United Kingdom) [presenting]
Gary Koop - University of Strathclyde (United Kingdom)
Stuart McIntyre - University of Strathclyde (United Kingdom)
James Mitchell - University of Warwick (United Kingdom)
Abstract: Models are developed for regional nowcasting by producing monthly nowcasts and historical estimates of GDP growth at the US state level using a Mixed frequency Vector Autoregression (MF-VAR). MF-VARs have enjoyed great popularity in policy circles since they can provide timely, high frequency nowcasts of low frequency variables such as GDP growth which are released with a delay. A common set-up is to nowcast a quarterly variable (e.g. GDP growth) using several monthly variables. Nowcasting state level GDP growth is more of a challenge since there are 51 (50 states plus District of Columbia) variables to be nowcast and the frequency mismatch is more complicated (i.e. we have a three-way frequency mismatch involving annual, quarterly and monthly variables) and changes over time. We work with MF-VARs which are much larger than is conventional, involve many more, and more complicated, latent states. This raises challenges in terms of over-parameterization concerns and the computational burden. We develop a Bayesian modelling framework which overcomes these challenges and present results on the accuracy of nowcasts of real-time economic growth in the USA from 2006 to 2019 at the monthly frequency.