Nowcasting GDP in Parsimony Data Environment Using Bayesian Mixed Frequency VAR (Empirical Evidence from Syria)

Monitoring economic conditions in real-time or Nowcasting is among the most important tasks routinely performed by economists as it is important in describing the investment environment in any country. Nowcasting brings some key challenges that characterize modern frugal data analyses in developing countries, often referred to as the three (V)s. These include: the small number of continuously published time series (volume), the complexity of the data covering different sectors of the economy and being asynchronous with different frequency and accuracy to be published (variety), and the need to incorporate new information within months of its publication (velocity). In this article, we explored alternative ways to use Bayesian Mixed Frequency Vector Autoregressive (BMFVAR) models to address these challenges. The research found that BMFVAR can effectively handle the three (V)s and create real-time accurate probabilistic forecasts of the Syrian economic activity and, beyond that, a powerful narrative via scenario analysis.